Fix remaining -m<module> patterns in Python code and scripts for consistency

Co-authored-by: malfet <2453524+malfet@users.noreply.github.com>

.ci/docker/almalinux/Dockerfile

@@ -20,7 +20,7 @@ ENV PATH=/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/bin:$PATH
 
 # cmake-3.18.4 from pip
 RUN yum install -y python3-pip && \
-    python3 -mpip install cmake==3.18.4 && \
+    python3 -m pip install cmake==3.18.4 && \
     ln -s /usr/local/bin/cmake /usr/bin/cmake3
 RUN rm -rf /usr/local/cuda-*
 

.ci/docker/common/install_cpython.sh

@@ -83,6 +83,10 @@ function build_cpython {
         py_suffix=${py_ver::-1}
         py_folder=$py_suffix
     fi
+    # Update to rc2 due to https://github.com/python/cpython/commit/c72699086fe4
+    if [ "$py_suffix" == "3.14.0" ]; then
+        py_suffix="3.14.0rc2"
+    fi
     wget -q $PYTHON_DOWNLOAD_URL/$py_folder/Python-$py_suffix.tgz -O Python-$py_ver.tgz
     do_cpython_build $py_ver Python-$py_suffix
 

.ci/docker/common/install_inductor_benchmark_deps.sh

@@ -25,7 +25,7 @@ function install_torchbench() {
   python install.py --continue_on_fail
 
   echo "Print all dependencies after TorchBench is installed"
-  python -mpip freeze
+  python -m pip freeze
   popd
 
   chown -R jenkins torchbench

.ci/docker/common/install_mkl.sh

@@ -8,8 +8,8 @@ MKLROOT=/opt/intel
 mkdir -p ${MKLROOT}
 pushd /tmp
 
-python3 -mpip install wheel
-python3 -mpip download -d . mkl-static==${MKL_VERSION}
+python3 -m pip install wheel
+python3 -m pip download -d . mkl-static==${MKL_VERSION}
 python3 -m wheel unpack mkl_static-${MKL_VERSION}-py2.py3-none-manylinux1_x86_64.whl
 python3 -m wheel unpack mkl_include-${MKL_VERSION}-py2.py3-none-manylinux1_x86_64.whl
 mv mkl_static-${MKL_VERSION}/mkl_static-${MKL_VERSION}.data/data/lib ${MKLROOT}

.ci/docker/common/install_onnx.sh

@@ -19,7 +19,7 @@ pip_install \
   transformers==4.36.2
 
 pip_install coloredlogs packaging
-pip_install onnxruntime==1.23.1
+pip_install onnxruntime==1.23.0
 pip_install onnxscript==0.5.4
 
 # Cache the transformers model to be used later by ONNX tests. We need to run the transformers

.ci/docker/common/install_python.sh

@@ -11,5 +11,5 @@ ln -s /usr/bin/python${PYTHON_VERSION} /usr/bin/python
 python -m venv /var/lib/jenkins/ci_env
 source /var/lib/jenkins/ci_env/bin/activate
 
-python -mpip install --upgrade pip
-python -mpip install -r /opt/requirements-ci.txt
+python -m pip install --upgrade pip
+python -m pip install -r /opt/requirements-ci.txt

.ci/docker/libtorch/build.sh

@@ -39,13 +39,9 @@ case ${DOCKER_TAG_PREFIX} in
         DOCKER_GPU_BUILD_ARG=""
         ;;
     rocm*)
-        # we want the patch version of 7.0 instead
-        if [[ "$GPU_ARCH_VERSION" == *"7.0"* ]]; then
-            GPU_ARCH_VERSION="${GPU_ARCH_VERSION}.2"
-        fi
         # we want the patch version of 6.4 instead
         if [[ "$GPU_ARCH_VERSION" == *"6.4"* ]]; then
-            GPU_ARCH_VERSION="${GPU_ARCH_VERSION}.4"
+            GPU_ARCH_VERSION="${GPU_ARCH_VERSION}.2"
         fi
         BASE_TARGET=rocm
         GPU_IMAGE=rocm/dev-ubuntu-22.04:${GPU_ARCH_VERSION}-complete

.ci/docker/manywheel/Dockerfile_2_28

@@ -14,7 +14,7 @@ ENV LD_LIBRARY_PATH=/opt/rh/gcc-toolset-${DEVTOOLSET_VERSION}/root/usr/lib64:/op
 
 # cmake-3.18.4 from pip
 RUN yum install -y python3-pip && \
-    python3 -mpip install cmake==3.18.4 && \
+    python3 -m pip install cmake==3.18.4 && \
     ln -s /usr/local/bin/cmake /usr/bin/cmake3
 
 FROM base as openssl
@@ -135,7 +135,7 @@ RUN bash ./patch_libstdc.sh && rm patch_libstdc.sh
 
 # cmake-3.18.4 from pip; force in case cmake3 already exists
 RUN yum install -y python3-pip && \
-    python3 -mpip install cmake==3.18.4 && \
+    python3 -m pip install cmake==3.18.4 && \
     ln -sf /usr/local/bin/cmake /usr/bin/cmake3
 
 FROM cpu_final as cuda_final
@@ -157,7 +157,7 @@ ENV ROCM_PATH /opt/rocm
 # cmake-3.28.4 from pip to get enable_language(HIP)
 # and avoid 3.21.0 cmake+ninja issues with ninja inserting "-Wl,--no-as-needed" in LINK_FLAGS for static linker
 RUN python3 -m pip install --upgrade pip && \
-    python3 -mpip install cmake==3.28.4
+    python3 -m pip install cmake==3.28.4
 # replace the libdrm in /opt/amdgpu with custom amdgpu.ids lookup path
 ADD ./common/install_rocm_drm.sh install_rocm_drm.sh
 RUN bash ./install_rocm_drm.sh && rm install_rocm_drm.sh
@@ -174,7 +174,7 @@ FROM cpu_final as xpu_final
 ENV XPU_DRIVER_TYPE ROLLING
 # cmake-3.28.4 from pip
 RUN python3 -m pip install --upgrade pip && \
-    python3 -mpip install cmake==3.28.4
+    python3 -m pip install cmake==3.28.4
 ADD ./common/install_xpu.sh install_xpu.sh
 ENV XPU_VERSION 2025.2
 RUN bash ./install_xpu.sh && rm install_xpu.sh

.ci/docker/manywheel/Dockerfile_s390x

@@ -113,7 +113,7 @@ RUN dnf install -y \
 RUN env GRPC_PYTHON_BUILD_SYSTEM_OPENSSL=True pip3 install grpcio
 
 # cmake-3.28.0 from pip for onnxruntime
-RUN python3 -mpip install cmake==3.28.0
+RUN python3 -m pip install cmake==3.28.0
 
 ADD ./common/patch_libstdc.sh patch_libstdc.sh
 RUN bash ./patch_libstdc.sh && rm patch_libstdc.sh

.ci/docker/manywheel/build.sh

@@ -75,13 +75,9 @@ case ${image} in
         DOCKERFILE_SUFFIX="_cuda_aarch64"
         ;;
     manylinux2_28-builder:rocm*)
-        # we want the patch version of 7.0 instead
-        if [[ "$GPU_ARCH_VERSION" == *"7.0"* ]]; then
-            GPU_ARCH_VERSION="${GPU_ARCH_VERSION}.2"
-        fi
         # we want the patch version of 6.4 instead
         if [[ "$GPU_ARCH_VERSION" == *"6.4"* ]]; then
-            GPU_ARCH_VERSION="${GPU_ARCH_VERSION}.4"
+            GPU_ARCH_VERSION="${GPU_ARCH_VERSION}.2"
         fi
         TARGET=rocm_final
         MANY_LINUX_VERSION="2_28"

.ci/docker/requirements-ci.txt

@@ -334,12 +334,12 @@ sympy==1.13.3
 #Pinned versions:
 #test that import:
 
-onnx==1.19.1
+onnx==1.18.0
 #Description: Required by onnx tests, and mypy and test_public_bindings.py when checking torch.onnx._internal
 #Pinned versions:
 #test that import:
 
-onnxscript==0.5.4
+onnxscript==0.5.3
 #Description: Required by mypy and test_public_bindings.py when checking torch.onnx._internal
 #Pinned versions:
 #test that import:

.ci/lumen_cli/cli/lib/common/git_helper.py

@@ -57,8 +57,8 @@ def clone_external_repo(target: str, repo: str, dst: str = "", update_submodules
         logger.info("Successfully cloned %s", target)
         return r, commit
 
-    except GitCommandError:
-        logger.exception("Git operation failed")
+    except GitCommandError as e:
+        logger.error("Git operation failed: %s", e)
         raise
 
 

.ci/lumen_cli/pyproject.toml

@@ -6,7 +6,7 @@ dependencies = [
     "GitPython==3.1.45",
     "docker==7.1.0",
     "pytest==7.3.2",
-    "uv==0.9.5"
+    "uv==0.8.6"
 ]
 
 [tool.setuptools]

.ci/pytorch/build.sh

@@ -288,7 +288,7 @@ else
     # or building non-XLA tests.
     if [[ "$BUILD_ENVIRONMENT" != *rocm*  && "$BUILD_ENVIRONMENT" != *xla* && "$BUILD_ENVIRONMENT" != *riscv64* ]]; then
       # Install numpy-2.0.2 for builds which are backward compatible with 1.X
-      python -mpip install numpy==2.0.2
+      python -m pip install numpy==2.0.2
 
       WERROR=1 python setup.py clean
 

.ci/pytorch/common_utils.sh

@@ -67,13 +67,13 @@ function pip_install_whl() {
     # Loop through each path and install individually
     for path in "${paths[@]}"; do
       echo "Installing $path"
-      python3 -mpip install --no-index --no-deps "$path"
+      python3 -m pip install --no-index --no-deps "$path"
     done
   else
     # Loop through each argument and install individually
     for path in "${args[@]}"; do
       echo "Installing $path"
-      python3 -mpip install --no-index --no-deps "$path"
+      python3 -m pip install --no-index --no-deps "$path"
     done
   fi
 }

.ci/pytorch/macos-test.sh

@@ -182,7 +182,7 @@ checkout_install_torchbench() {
   pip uninstall -y torchao
 
   echo "Print all dependencies after TorchBench is installed"
-  python -mpip freeze
+  python -m pip freeze
 }
 
 torchbench_setup_macos() {
@@ -211,7 +211,7 @@ torchbench_setup_macos() {
 }
 
 pip_benchmark_deps() {
-  python -mpip install --no-input requests cython scikit-learn six
+  python -m pip install --no-input requests cython scikit-learn six
 }
 
 

.ci/pytorch/test.sh

@@ -1434,7 +1434,7 @@ EOF
   # shellcheck source=./common-build.sh
   source "$(dirname "${BASH_SOURCE[0]}")/common-build.sh"
   python -m build --wheel --no-isolation -C--build-option=--bdist-dir="base_bdist_tmp" --outdir "base_dist"
-  python -mpip install base_dist/*.whl
+  python -m pip install base_dist/*.whl
   echo "::endgroup::"
 
   pushd test/forward_backward_compatibility

.ci/wheel/build_wheel.sh

@@ -173,7 +173,7 @@ esac
 PINNED_PACKAGES=(
     "numpy${NUMPY_PINNED_VERSION}"
 )
-python -mvenv ~/${desired_python}-build
+python -m venv ~/${desired_python}-build
 source ~/${desired_python}-build/bin/activate
 retry pip install "${PINNED_PACKAGES[@]}" -r "${pytorch_rootdir}/requirements.txt"
 retry brew install libomp

.circleci/scripts/binary_populate_env.sh

@@ -163,13 +163,8 @@ if [[ "$(uname)" != Darwin ]]; then
   MEMORY_LIMIT_MAX_JOBS=12
   NUM_CPUS=$(( $(nproc) - 2 ))
 
-  if [[ "$(uname)" == Linux ]]; then
-    # Defaults here for **binary** linux builds so they can be changed in one place
-    export MAX_JOBS=${MAX_JOBS:-$(( ${NUM_CPUS} > ${MEMORY_LIMIT_MAX_JOBS} ? ${MEMORY_LIMIT_MAX_JOBS} : ${NUM_CPUS} ))}
-  else
-    # For other builds
-    export MAX_JOBS=${NUM_CPUS}
-  fi
+  # Defaults here for **binary** linux builds so they can be changed in one place
+  export MAX_JOBS=${MAX_JOBS:-$(( ${NUM_CPUS} > ${MEMORY_LIMIT_MAX_JOBS} ? ${MEMORY_LIMIT_MAX_JOBS} : ${NUM_CPUS} ))}
 
   cat >>"$envfile" <<EOL
   export MAX_JOBS="${MAX_JOBS}"

.claude/skills/pytorch-docstring.md

@@ -1,354 +0,0 @@
-# PyTorch Docstring Writing Guide
-
-This skill describes how to write docstrings for functions and methods in the PyTorch project, following the conventions in `torch/_tensor_docs.py` and `torch/nn/functional.py`.
-
-## General Principles
-
-- Use **raw strings** (`r"""..."""`) for all docstrings to avoid issues with LaTeX/math backslashes
-- Follow **Sphinx/reStructuredText** (reST) format for documentation
-- Be **concise but complete** - include all essential information
-- Always include **examples** when possible
-- Use **cross-references** to related functions/classes
-
-## Docstring Structure
-
-### 1. Function Signature (First Line)
-
-Start with the function signature showing all parameters:
-
-```python
-r"""function_name(param1, param2, *, kwarg1=default1, kwarg2=default2) -> ReturnType
-```
-
-**Notes:**
-- Include the function name
-- Show positional and keyword-only arguments (use `*` separator)
-- Include default values
-- Show return type annotation
-- This line should NOT end with a period
-
-### 2. Brief Description
-
-Provide a one-line description of what the function does:
-
-```python
-r"""conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) -> Tensor
-
-Applies a 2D convolution over an input image composed of several input
-planes.
-```
-
-### 3. Mathematical Formulas (if applicable)
-
-Use Sphinx math directives for mathematical expressions:
-
-```python
-.. math::
-    \text{Softmax}(x_{i}) = \frac{\exp(x_i)}{\sum_j \exp(x_j)}
-```
-
-Or inline math: `:math:\`x^2\``
-
-### 4. Cross-References
-
-Link to related classes and functions using Sphinx roles:
-
-- `:class:\`~torch.nn.ModuleName\`` - Link to a class
-- `:func:\`torch.function_name\`` - Link to a function
-- `:meth:\`~Tensor.method_name\`` - Link to a method
-- `:attr:\`attribute_name\`` - Reference an attribute
-- The `~` prefix shows only the last component (e.g., `Conv2d` instead of `torch.nn.Conv2d`)
-
-**Example:**
-```python
-See :class:`~torch.nn.Conv2d` for details and output shape.
-```
-
-### 5. Notes and Warnings
-
-Use admonitions for important information:
-
-```python
-.. note::
-    This function doesn't work directly with NLLLoss,
-    which expects the Log to be computed between the Softmax and itself.
-    Use log_softmax instead (it's faster and has better numerical properties).
-
-.. warning::
-    :func:`new_tensor` always copies :attr:`data`. If you have a Tensor
-    ``data`` and want to avoid a copy, use :func:`torch.Tensor.requires_grad_`
-    or :func:`torch.Tensor.detach`.
-```
-
-### 6. Args Section
-
-Document all parameters with type annotations and descriptions:
-
-```python
-Args:
-    input (Tensor): input tensor of shape :math:`(\text{minibatch} , \text{in\_channels} , iH , iW)`
-    weight (Tensor): filters of shape :math:`(\text{out\_channels} , kH , kW)`
-    bias (Tensor, optional): optional bias tensor of shape :math:`(\text{out\_channels})`. Default: ``None``
-    stride (int or tuple): the stride of the convolving kernel. Can be a single number or a
-      tuple `(sH, sW)`. Default: 1
-```
-
-**Formatting rules:**
-- Parameter name in **lowercase**
-- Type in parentheses: `(Type)`, `(Type, optional)` for optional parameters
-- Description follows the type
-- For optional parameters, include "Default: ``value``" at the end
-- Use double backticks for inline code: ``` ``None`` ```
-- Indent continuation lines by 2 spaces
-
-### 7. Keyword Args Section (if applicable)
-
-Sometimes keyword arguments are documented separately:
-
-```python
-Keyword args:
-    dtype (:class:`torch.dtype`, optional): the desired type of returned tensor.
-        Default: if None, same :class:`torch.dtype` as this tensor.
-    device (:class:`torch.device`, optional): the desired device of returned tensor.
-        Default: if None, same :class:`torch.device` as this tensor.
-    requires_grad (bool, optional): If autograd should record operations on the
-        returned tensor. Default: ``False``.
-```
-
-### 8. Returns Section (if needed)
-
-Document the return value:
-
-```python
-Returns:
-    Tensor: Sampled tensor of same shape as `logits` from the Gumbel-Softmax distribution.
-        If ``hard=True``, the returned samples will be one-hot, otherwise they will
-        be probability distributions that sum to 1 across `dim`.
-```
-
-Or simply include it in the function signature line if obvious from context.
-
-### 9. Examples Section
-
-Always include examples when possible:
-
-```python
-Examples::
-
-    >>> inputs = torch.randn(33, 16, 30)
-    >>> filters = torch.randn(20, 16, 5)
-    >>> F.conv1d(inputs, filters)
-
-    >>> # With square kernels and equal stride
-    >>> filters = torch.randn(8, 4, 3, 3)
-    >>> inputs = torch.randn(1, 4, 5, 5)
-    >>> F.conv2d(inputs, filters, padding=1)
-```
-
-**Formatting rules:**
-- Use `Examples::` with double colon
-- Use `>>>` prompt for Python code
-- Include comments with `#` when helpful
-- Show actual output when it helps understanding (indent without `>>>`)
-
-### 10. External References
-
-Link to papers or external documentation:
-
-```python
-.. _Link Name:
-    https://arxiv.org/abs/1611.00712
-```
-
-Reference them in text: ```See `Link Name`_```
-
-## Method Types
-
-### Native Python Functions
-
-For regular Python functions, use a standard docstring:
-
-```python
-def relu(input: Tensor, inplace: bool = False) -> Tensor:
-    r"""relu(input, inplace=False) -> Tensor
-
-    Applies the rectified linear unit function element-wise. See
-    :class:`~torch.nn.ReLU` for more details.
-    """
-    # implementation
-```
-
-### C-Bound Functions (using add_docstr)
-
-For C-bound functions, use `_add_docstr`:
-
-```python
-conv1d = _add_docstr(
-    torch.conv1d,
-    r"""
-conv1d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1) -> Tensor
-
-Applies a 1D convolution over an input signal composed of several input
-planes.
-
-See :class:`~torch.nn.Conv1d` for details and output shape.
-
-Args:
-    input: input tensor of shape :math:`(\text{minibatch} , \text{in\_channels} , iW)`
-    weight: filters of shape :math:`(\text{out\_channels} , kW)`
-    ...
-""",
-)
-```
-
-### In-Place Variants
-
-For in-place operations (ending with `_`), reference the original:
-
-```python
-add_docstr_all(
-    "abs_",
-    r"""
-abs_() -> Tensor
-
-In-place version of :meth:`~Tensor.abs`
-""",
-)
-```
-
-### Alias Functions
-
-For aliases, simply reference the original:
-
-```python
-add_docstr_all(
-    "absolute",
-    r"""
-absolute() -> Tensor
-
-Alias for :func:`abs`
-""",
-)
-```
-
-## Common Patterns
-
-### Shape Documentation
-
-Use LaTeX math notation for tensor shapes:
-
-```python
-:math:`(\text{minibatch} , \text{in\_channels} , iH , iW)`
-```
-
-### Reusable Argument Definitions
-
-For commonly used arguments, define them once and reuse:
-
-```python
-common_args = parse_kwargs(
-    """
-    dtype (:class:`torch.dtype`, optional): the desired type of returned tensor.
-        Default: if None, same as this tensor.
-"""
-)
-
-# Then use with .format():
-r"""
-...
-
-Keyword args:
-    {dtype}
-    {device}
-""".format(**common_args)
-```
-
-### Template Insertion
-
-Insert reproducibility notes or other common text:
-
-```python
-r"""
-{tf32_note}
-
-{cudnn_reproducibility_note}
-""".format(**reproducibility_notes, **tf32_notes)
-```
-
-## Complete Example
-
-Here's a complete example showing all elements:
-
-```python
-def gumbel_softmax(
-    logits: Tensor,
-    tau: float = 1,
-    hard: bool = False,
-    eps: float = 1e-10,
-    dim: int = -1,
-) -> Tensor:
-    r"""
-    Sample from the Gumbel-Softmax distribution and optionally discretize.
-
-    Args:
-        logits (Tensor): `[..., num_features]` unnormalized log probabilities
-        tau (float): non-negative scalar temperature
-        hard (bool): if ``True``, the returned samples will be discretized as one-hot vectors,
-              but will be differentiated as if it is the soft sample in autograd. Default: ``False``
-        dim (int): A dimension along which softmax will be computed. Default: -1
-
-    Returns:
-        Tensor: Sampled tensor of same shape as `logits` from the Gumbel-Softmax distribution.
-            If ``hard=True``, the returned samples will be one-hot, otherwise they will
-            be probability distributions that sum to 1 across `dim`.
-
-    .. note::
-        This function is here for legacy reasons, may be removed from nn.Functional in the future.
-
-    Examples::
-        >>> logits = torch.randn(20, 32)
-        >>> # Sample soft categorical using reparametrization trick:
-        >>> F.gumbel_softmax(logits, tau=1, hard=False)
-        >>> # Sample hard categorical using "Straight-through" trick:
-        >>> F.gumbel_softmax(logits, tau=1, hard=True)
-
-    .. _Link 1:
-        https://arxiv.org/abs/1611.00712
-    """
-    # implementation
-```
-
-## Quick Checklist
-
-When writing a PyTorch docstring, ensure:
-
-- [ ] Use raw string (`r"""`)
-- [ ] Include function signature on first line
-- [ ] Provide brief description
-- [ ] Document all parameters in Args section with types
-- [ ] Include default values for optional parameters
-- [ ] Use Sphinx cross-references (`:func:`, `:class:`, `:meth:`)
-- [ ] Add mathematical formulas if applicable
-- [ ] Include at least one example in Examples section
-- [ ] Add warnings/notes for important caveats
-- [ ] Link to related module class with `:class:`
-- [ ] Use proper math notation for tensor shapes
-- [ ] Follow consistent formatting and indentation
-
-## Common Sphinx Roles Reference
-
-- `:class:\`~torch.nn.Module\`` - Class reference
-- `:func:\`torch.function\`` - Function reference
-- `:meth:\`~Tensor.method\`` - Method reference
-- `:attr:\`attribute\`` - Attribute reference
-- `:math:\`equation\`` - Inline math
-- `:ref:\`label\`` - Internal reference
-- ``` ``code`` ``` - Inline code (use double backticks)
-
-## Additional Notes
-
-- **Indentation**: Use 4 spaces for code, 2 spaces for continuation of parameter descriptions
-- **Line length**: Try to keep lines under 100 characters when possible
-- **Periods**: End sentences with periods, but not the signature line
-- **Backticks**: Use double backticks for code: ``` ``True`` ``None`` ``False`` ```
-- **Types**: Common types are `Tensor`, `int`, `float`, `bool`, `str`, `tuple`, `list`, etc.

.flake8

@@ -7,12 +7,16 @@ max-line-length = 120
 # C408 ignored because we like the dict keyword argument syntax
 # E501 is not flexible enough, we're using B950 instead
 ignore =
-    E203,E305,E402,E501,E704,E741,F405,F841,F999,W503,W504,C408,E302,W291,E303,F824,
+    E203,E305,E402,E501,E704,E721,E741,F405,F841,F999,W503,W504,C408,E302,W291,E303,F824,
     # shebang has extra meaning in fbcode lints, so I think it's not worth trying
     # to line this up with executable bit
     EXE001,
     # these ignores are from flake8-bugbear; please fix!
     B007,B008,B017,B019,B023,B028,B903,B905,B906,B907,B908,B910
+    # these ignores are from flake8-comprehensions; please fix!
+    C407,
+    # these ignores are from flake8-logging-format; please fix!
+    G100,G101,G200
     # these ignores are from flake8-simplify. please fix or ignore with commented reason
     SIM105,SIM108,SIM110,SIM111,SIM113,SIM114,SIM115,SIM116,SIM117,SIM118,SIM119,SIM12,
     # SIM104 is already covered by pyupgrade ruff

.github/actions/setup-rocm/action.yml

@@ -124,10 +124,3 @@ runs:
       id: login-ecr
       continue-on-error: true
       uses: aws-actions/amazon-ecr-login@062b18b96a7aff071d4dc91bc00c4c1a7945b076 # v2.0.1
-
-    - name: Preserve github env variables for use in docker
-      shell: bash
-      run: |
-        env | grep '^GITHUB' >> "${RUNNER_TEMP}/github_env_${GITHUB_RUN_ID}"
-        env | grep '^CI' >> "${RUNNER_TEMP}/github_env_${GITHUB_RUN_ID}"
-        env | grep '^RUNNER' >> "${RUNNER_TEMP}/github_env_${GITHUB_RUN_ID}"

.github/ci_commit_pins/audio.txt

@@ -1 +1 @@
-69bbe7363897764f9e758d851cd0340147d27f94
+1b013f5b5a87a1882eb143c26d79d091150d6a37

.github/ci_commit_pins/vision.txt

@@ -1 +1 @@
-1752fe6809b74921644866275ab80244b96e80bc
+faffd5cf673615583da6517275e361cb3dbc77e6

.github/ci_configs/vllm/Dockerfile

@@ -283,9 +283,6 @@ RUN --mount=type=bind,source=${TORCH_WHEELS_PATH},target=/dist \
         uv pip install --system $(cat torch_build_versions.txt | xargs) --index-url https://download.pytorch.org/whl/nightly/cu$(echo $CUDA_VERSION | cut -d. -f1,2 | tr -d '.'); \
     fi
 
-RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip install --system --pre apache-tvm-ffi==0.1.0b15
-
 # Install the vllm wheel from previous stage
 RUN --mount=type=cache,target=/root/.cache/uv \
     uv pip install --system /wheels/vllm/*.whl --verbose
@@ -298,8 +295,6 @@ RUN --mount=type=cache,target=/root/.cache/uv \
 ARG torch_cuda_arch_list='8.0;8.9;9.0a;10.0a;12.0'
 ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
 
-# TODO(elainewy): remove this once vllm commit is updated, and install flashinfer from pip
-# see https://github.com/pytorch/pytorch/pull/165274#issuecomment-3408531784
 ARG FLASHINFER_GIT_REPO="https://github.com/flashinfer-ai/flashinfer.git"
 ARG FLASHINFER_GIT_REF="v0.2.14.post1"
 

.github/labeler.yml

@@ -133,32 +133,3 @@
 
 "ciflow/vllm":
 - .github/ci_commit_pins/vllm.txt
-
-"ciflow/b200":
-- test/test_matmul_cuda.py
-- test/test_scaled_matmul_cuda.py
-- test/inductor/test_fp8.py
-- aten/src/ATen/native/cuda/Blas.cpp
-- torch/**/*cublas*
-- torch/_inductor/kernel/mm.py
-- test/inductor/test_max_autotune.py
-- third_party/fbgemm
-
-"ciflow/h100":
-- test/test_matmul_cuda.py
-- test/test_scaled_matmul_cuda.py
-- test/inductor/test_fp8.py
-- aten/src/ATen/native/cuda/Blas.cpp
-- torch/**/*cublas*
-- torch/_inductor/kernel/mm.py
-- test/inductor/test_max_autotune.py
-- third_party/fbgemm
-
-"ciflow/rocm":
-- test/test_matmul_cuda.py
-- test/test_scaled_matmul_cuda.py
-- test/inductor/test_fp8.py
-- aten/src/ATen/native/cuda/Blas.cpp
-- torch/_inductor/kernel/mm.py
-- test/inductor/test_max_autotune.py
-- third_party/fbgemm

.github/pytorch-probot.yml

@@ -33,7 +33,6 @@ ciflow_push_tags:
 - ciflow/rocm
 - ciflow/rocm-mi300
 - ciflow/rocm-mi355
-- ciflow/rocm-navi31
 - ciflow/s390
 - ciflow/slow
 - ciflow/torchbench

.github/scripts/generate_binary_build_matrix.py

@@ -79,21 +79,21 @@ PYTORCH_EXTRA_INSTALL_REQUIREMENTS = {
         "nvidia-cufile-cu12==1.13.1.3; platform_system == 'Linux'"
     ),
     "12.9": (
-        "nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | "
-        "nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | "
-        "nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | "
-        "nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | "
-        "nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | "
-        "nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | "
-        "nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | "
-        "nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | "
-        "nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | "
-        "nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | "
-        "nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | "
-        "nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | "
-        "nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | "
-        "nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | "
-        "nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'"
+        "nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | "
+        "nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | "
+        "nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | "
+        "nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' and platform_machine == 'x86_64' | "
+        "nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | "
+        "nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | "
+        "nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' and platform_machine == 'x86_64' | "
+        "nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' and platform_machine == 'x86_64' | "
+        "nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' and platform_machine == 'x86_64' | "
+        "nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' and platform_machine == 'x86_64' | "
+        "nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' and platform_machine == 'x86_64' | "
+        "nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' and platform_machine == 'x86_64' | "
+        "nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | "
+        "nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | "
+        "nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux' and platform_machine == 'x86_64'"
     ),
     "13.0": (
         "nvidia-cuda-nvrtc==13.0.48; platform_system == 'Linux' | "

.github/scripts/prepare_vllm_wheels.sh

@@ -24,7 +24,7 @@ change_wheel_version() {
   local t_version=$4
 
   # Extract the wheel
-  ${PYTHON_EXECUTABLE} -mwheel unpack $wheel
+  ${PYTHON_EXECUTABLE} -m wheel unpack $wheel
 
   mv "${package}-${f_version}" "${package}-${t_version}"
   # Change the version from f_version to t_version in the dist-info dir
@@ -47,7 +47,7 @@ change_wheel_version() {
   popd
 
   # Repack the wheel
-  ${PYTHON_EXECUTABLE} -mwheel pack "${package}-${t_version}"
+  ${PYTHON_EXECUTABLE} -m wheel pack "${package}-${t_version}"
 
   # Clean up
   rm -rf "${package}-${t_version}"
@@ -85,7 +85,7 @@ repackage_wheel() {
 }
 
 # Require to re-package the wheel
-${PYTHON_EXECUTABLE} -mpip install wheel==0.45.1
+${PYTHON_EXECUTABLE} -m pip install wheel==0.45.1
 
 pushd externals/vllm/wheels
 for package in xformers flashinfer-python vllm; do

.github/templates/macos_binary_build_workflow.yml.j2

@@ -26,8 +26,9 @@ name: !{{ build_environment }}
       - name: Setup Python
         uses: actions/setup-python@v6
         with:
+          # TODO: Removeme once 3.14 is out
           # .4 version is min minor for 3.10, and also no-gil version of 3.13 needs at least 3.13.3
-          python-version: "!{{ py_ver.strip('t') + ('.4' if '3.14' not in py_ver else '.0') }}"
+          python-version: "!{{ (py_ver.strip('t') + '.4') if '3.14' not in py_ver else '3.14.0-rc.2' }}"
           freethreaded: !{{ "true" if py_ver.endswith('t') else "false" }}
 {%- endmacro %}
 

.github/templates/windows_binary_build_workflow.yml.j2

@@ -79,9 +79,9 @@ jobs:
     runs-on: "windows-11-arm64-preview"
     {%- else %}
     {%- if branches == "nightly" %}
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     {%- else %}
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge.nonephemeral"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge.nonephemeral"
     {%- endif %}
     {%- endif %}
     timeout-minutes: !{{ common.timeout_minutes_windows_binary }}

.github/workflows/_mac-test.yml

@@ -211,7 +211,7 @@ jobs:
             $tool --version
           done
 
-          python3 -mpip install --no-index --no-deps dist/*.whl
+          python3 -m pip install --no-index --no-deps dist/*.whl
 
           set +e
           pushd "${RUNNER_TEMP}"
@@ -222,7 +222,7 @@ jobs:
           popd
 
           if [ "${RC}" -ne 0 ]; then
-            python3 -mpip install --ignore-installed -r "${PIP_REQUIREMENTS_FILE}"
+            python3 -m pip install --ignore-installed -r "${PIP_REQUIREMENTS_FILE}"
           fi
           set -e
 

.github/workflows/_win-test.yml

@@ -204,7 +204,7 @@ jobs:
         run: |
           pushd "${PYTORCH_FINAL_PACKAGE_DIR}"
           # shellcheck disable=SC2046,SC2102
-          python3 -mpip install $(echo *.whl)[opt-einsum,optree] optree==0.13.0
+          python3 -m pip install $(echo *.whl)[opt-einsum,optree] optree==0.13.0
           popd
 
           .ci/pytorch/win-test.sh

.github/workflows/build-vllm-wheel.yml

@@ -126,13 +126,13 @@ jobs:
             "${MANYLINUX_IMAGE}"
           )
 
-          docker exec -t "${container_name}" "${PYTHON_EXECUTABLE}" -mpip install \
+          docker exec -t "${container_name}" "${PYTHON_EXECUTABLE}" -m pip install \
             --pre torch torchvision torchaudio \
             --index-url "https://download.pytorch.org/whl/nightly/${BUILD_DEVICE}"
 
           # I wonder if there is a command to both download and install the wheels
           # in one go
-          docker exec -t "${container_name}" "${PYTHON_EXECUTABLE}" -mpip download \
+          docker exec -t "${container_name}" "${PYTHON_EXECUTABLE}" -m pip download \
             --pre torch torchvision torchaudio \
             --index-url "https://download.pytorch.org/whl/nightly/${BUILD_DEVICE}"
 

.github/workflows/generated-linux-aarch64-binary-manywheel-nightly.yml

@@ -224,7 +224,7 @@ jobs:
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_10-cuda-aarch64-12_9
       build_environment: linux-aarch64-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux' and platform_machine == 'x86_64'
       timeout-minutes: 420
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
@@ -473,7 +473,7 @@ jobs:
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_11-cuda-aarch64-12_9
       build_environment: linux-aarch64-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux' and platform_machine == 'x86_64'
       timeout-minutes: 420
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
@@ -722,7 +722,7 @@ jobs:
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_12-cuda-aarch64-12_9
       build_environment: linux-aarch64-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux' and platform_machine == 'x86_64'
       timeout-minutes: 420
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
@@ -971,7 +971,7 @@ jobs:
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_13-cuda-aarch64-12_9
       build_environment: linux-aarch64-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux' and platform_machine == 'x86_64'
       timeout-minutes: 420
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
@@ -1220,7 +1220,7 @@ jobs:
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_13t-cuda-aarch64-12_9
       build_environment: linux-aarch64-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux' and platform_machine == 'x86_64'
       timeout-minutes: 420
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
@@ -1469,7 +1469,7 @@ jobs:
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_14-cuda-aarch64-12_9
       build_environment: linux-aarch64-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux' and platform_machine == 'x86_64'
       timeout-minutes: 420
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
@@ -1718,7 +1718,7 @@ jobs:
       ALPINE_IMAGE: "arm64v8/alpine"
       build_name: manywheel-py3_14t-cuda-aarch64-12_9
       build_environment: linux-aarch64-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux' and platform_machine == 'x86_64'
       timeout-minutes: 420
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/generated-linux-binary-manywheel-nightly.yml

@@ -259,7 +259,7 @@ jobs:
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
       build_name: manywheel-py3_10-cuda12_9
       build_environment: linux-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux' and platform_machine == 'x86_64'
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
   manywheel-py3_10-cuda12_9-test:  # Testing
@@ -925,7 +925,7 @@ jobs:
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
       build_name: manywheel-py3_11-cuda12_9
       build_environment: linux-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux' and platform_machine == 'x86_64'
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
   manywheel-py3_11-cuda12_9-test:  # Testing
@@ -1591,7 +1591,7 @@ jobs:
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
       build_name: manywheel-py3_12-cuda12_9
       build_environment: linux-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux' and platform_machine == 'x86_64'
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
   manywheel-py3_12-cuda12_9-test:  # Testing
@@ -2257,7 +2257,7 @@ jobs:
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
       build_name: manywheel-py3_13-cuda12_9
       build_environment: linux-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux' and platform_machine == 'x86_64'
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
   manywheel-py3_13-cuda12_9-test:  # Testing
@@ -2923,7 +2923,7 @@ jobs:
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
       build_name: manywheel-py3_13t-cuda12_9
       build_environment: linux-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux' and platform_machine == 'x86_64'
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
   manywheel-py3_13t-cuda12_9-test:  # Testing
@@ -3589,7 +3589,7 @@ jobs:
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
       build_name: manywheel-py3_14-cuda12_9
       build_environment: linux-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux' and platform_machine == 'x86_64'
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
   manywheel-py3_14-cuda12_9-test:  # Testing
@@ -4255,7 +4255,7 @@ jobs:
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
       build_name: manywheel-py3_14t-cuda12_9
       build_environment: linux-binary-manywheel
-      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux'
+      PYTORCH_EXTRA_INSTALL_REQUIREMENTS: nvidia-cuda-nvrtc-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-runtime-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cuda-cupti-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cudnn-cu12==9.10.2.21; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cublas-cu12==12.9.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufft-cu12==11.4.1.4; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-curand-cu12==10.3.10.19; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusolver-cu12==11.7.5.82; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparse-cu12==12.5.10.65; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cusparselt-cu12==0.7.1; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nccl-cu12==2.27.5; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvshmem-cu12==3.3.20; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvtx-cu12==12.9.79; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-nvjitlink-cu12==12.9.86; platform_system == 'Linux' and platform_machine == 'x86_64' | nvidia-cufile-cu12==1.14.1.1; platform_system == 'Linux' and platform_machine == 'x86_64'
     secrets:
       github-token: ${{ secrets.GITHUB_TOKEN }}
   manywheel-py3_14t-cuda12_9-test:  # Testing

.github/workflows/generated-macos-arm64-binary-libtorch-release-nightly.yml

@@ -63,6 +63,7 @@ jobs:
       - name: Setup Python
         uses: actions/setup-python@v6
         with:
+          # TODO: Removeme once 3.14 is out
           # .4 version is min minor for 3.10, and also no-gil version of 3.13 needs at least 3.13.3
           python-version: "3.10.4"
           freethreaded: false

.github/workflows/generated-macos-arm64-binary-wheel-nightly.yml

@@ -59,6 +59,7 @@ jobs:
       - name: Setup Python
         uses: actions/setup-python@v6
         with:
+          # TODO: Removeme once 3.14 is out
           # .4 version is min minor for 3.10, and also no-gil version of 3.13 needs at least 3.13.3
           python-version: "3.10.4"
           freethreaded: false
@@ -105,7 +106,7 @@ jobs:
           SMOKE_TEST_PARAMS=""
 
           # shellcheck disable=SC2086
-          python -mvenv test_venv
+          python -m venv test_venv
           source test_venv/bin/activate
           pip install "$PYTORCH_FINAL_PACKAGE_DIR"/*.whl numpy -v
 
@@ -168,6 +169,7 @@ jobs:
       - name: Setup Python
         uses: actions/setup-python@v6
         with:
+          # TODO: Removeme once 3.14 is out
           # .4 version is min minor for 3.10, and also no-gil version of 3.13 needs at least 3.13.3
           python-version: "3.11.4"
           freethreaded: false
@@ -214,7 +216,7 @@ jobs:
           SMOKE_TEST_PARAMS=""
 
           # shellcheck disable=SC2086
-          python -mvenv test_venv
+          python -m venv test_venv
           source test_venv/bin/activate
           pip install "$PYTORCH_FINAL_PACKAGE_DIR"/*.whl numpy -v
 
@@ -277,6 +279,7 @@ jobs:
       - name: Setup Python
         uses: actions/setup-python@v6
         with:
+          # TODO: Removeme once 3.14 is out
           # .4 version is min minor for 3.10, and also no-gil version of 3.13 needs at least 3.13.3
           python-version: "3.12.4"
           freethreaded: false
@@ -323,7 +326,7 @@ jobs:
           SMOKE_TEST_PARAMS=""
 
           # shellcheck disable=SC2086
-          python -mvenv test_venv
+          python -m venv test_venv
           source test_venv/bin/activate
           pip install "$PYTORCH_FINAL_PACKAGE_DIR"/*.whl numpy -v
 
@@ -386,6 +389,7 @@ jobs:
       - name: Setup Python
         uses: actions/setup-python@v6
         with:
+          # TODO: Removeme once 3.14 is out
           # .4 version is min minor for 3.10, and also no-gil version of 3.13 needs at least 3.13.3
           python-version: "3.13.4"
           freethreaded: false
@@ -432,7 +436,7 @@ jobs:
           SMOKE_TEST_PARAMS=""
 
           # shellcheck disable=SC2086
-          python -mvenv test_venv
+          python -m venv test_venv
           source test_venv/bin/activate
           pip install "$PYTORCH_FINAL_PACKAGE_DIR"/*.whl numpy -v
 
@@ -495,6 +499,7 @@ jobs:
       - name: Setup Python
         uses: actions/setup-python@v6
         with:
+          # TODO: Removeme once 3.14 is out
           # .4 version is min minor for 3.10, and also no-gil version of 3.13 needs at least 3.13.3
           python-version: "3.13.4"
           freethreaded: true
@@ -541,7 +546,7 @@ jobs:
           SMOKE_TEST_PARAMS=""
 
           # shellcheck disable=SC2086
-          python -mvenv test_venv
+          python -m venv test_venv
           source test_venv/bin/activate
           pip install "$PYTORCH_FINAL_PACKAGE_DIR"/*.whl numpy -v
 
@@ -604,8 +609,9 @@ jobs:
       - name: Setup Python
         uses: actions/setup-python@v6
         with:
+          # TODO: Removeme once 3.14 is out
           # .4 version is min minor for 3.10, and also no-gil version of 3.13 needs at least 3.13.3
-          python-version: "3.14.0"
+          python-version: "3.14.0-rc.2"
           freethreaded: false
       - name: Checkout PyTorch
         uses: actions/checkout@v4
@@ -650,7 +656,7 @@ jobs:
           SMOKE_TEST_PARAMS=""
 
           # shellcheck disable=SC2086
-          python -mvenv test_venv
+          python -m venv test_venv
           source test_venv/bin/activate
           pip install "$PYTORCH_FINAL_PACKAGE_DIR"/*.whl numpy -v
 
@@ -713,8 +719,9 @@ jobs:
       - name: Setup Python
         uses: actions/setup-python@v6
         with:
+          # TODO: Removeme once 3.14 is out
           # .4 version is min minor for 3.10, and also no-gil version of 3.13 needs at least 3.13.3
-          python-version: "3.14.0"
+          python-version: "3.14.0-rc.2"
           freethreaded: true
       - name: Checkout PyTorch
         uses: actions/checkout@v4
@@ -759,7 +766,7 @@ jobs:
           SMOKE_TEST_PARAMS=""
 
           # shellcheck disable=SC2086
-          python -mvenv test_venv
+          python -m venv test_venv
           source test_venv/bin/activate
           pip install "$PYTORCH_FINAL_PACKAGE_DIR"/*.whl numpy -v
 

.github/workflows/generated-windows-binary-libtorch-debug-nightly.yml

@@ -44,7 +44,7 @@ jobs:
   libtorch-cpu-shared-with-deps-debug-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -291,7 +291,7 @@ jobs:
   libtorch-cuda12_6-shared-with-deps-debug-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -541,7 +541,7 @@ jobs:
   libtorch-cuda12_8-shared-with-deps-debug-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -791,7 +791,7 @@ jobs:
   libtorch-cuda13_0-shared-with-deps-debug-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch

.github/workflows/generated-windows-binary-libtorch-release-nightly.yml

@@ -44,7 +44,7 @@ jobs:
   libtorch-cpu-shared-with-deps-release-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -291,7 +291,7 @@ jobs:
   libtorch-cuda12_6-shared-with-deps-release-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -541,7 +541,7 @@ jobs:
   libtorch-cuda12_8-shared-with-deps-release-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -791,7 +791,7 @@ jobs:
   libtorch-cuda13_0-shared-with-deps-release-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch

.github/workflows/generated-windows-binary-wheel-nightly.yml

@@ -44,7 +44,7 @@ jobs:
   wheel-py3_10-cpu-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -279,7 +279,7 @@ jobs:
   wheel-py3_10-cuda12_6-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -517,7 +517,7 @@ jobs:
   wheel-py3_10-cuda12_8-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -755,7 +755,7 @@ jobs:
   wheel-py3_10-cuda13_0-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -993,7 +993,7 @@ jobs:
   wheel-py3_10-xpu-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -1229,7 +1229,7 @@ jobs:
   wheel-py3_11-cpu-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -1464,7 +1464,7 @@ jobs:
   wheel-py3_11-cuda12_6-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -1702,7 +1702,7 @@ jobs:
   wheel-py3_11-cuda12_8-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -1940,7 +1940,7 @@ jobs:
   wheel-py3_11-cuda13_0-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -2178,7 +2178,7 @@ jobs:
   wheel-py3_11-xpu-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -2414,7 +2414,7 @@ jobs:
   wheel-py3_12-cpu-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -2649,7 +2649,7 @@ jobs:
   wheel-py3_12-cuda12_6-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -2887,7 +2887,7 @@ jobs:
   wheel-py3_12-cuda12_8-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -3125,7 +3125,7 @@ jobs:
   wheel-py3_12-cuda13_0-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -3363,7 +3363,7 @@ jobs:
   wheel-py3_12-xpu-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -3599,7 +3599,7 @@ jobs:
   wheel-py3_13-cpu-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -3834,7 +3834,7 @@ jobs:
   wheel-py3_13-cuda12_6-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -4072,7 +4072,7 @@ jobs:
   wheel-py3_13-cuda12_8-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -4310,7 +4310,7 @@ jobs:
   wheel-py3_13-cuda13_0-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -4548,7 +4548,7 @@ jobs:
   wheel-py3_13-xpu-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -4784,7 +4784,7 @@ jobs:
   wheel-py3_13t-cpu-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -5019,7 +5019,7 @@ jobs:
   wheel-py3_13t-cuda12_6-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -5257,7 +5257,7 @@ jobs:
   wheel-py3_13t-cuda12_8-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -5495,7 +5495,7 @@ jobs:
   wheel-py3_13t-cuda13_0-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -5733,7 +5733,7 @@ jobs:
   wheel-py3_13t-xpu-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -5969,7 +5969,7 @@ jobs:
   wheel-py3_14-cpu-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -6204,7 +6204,7 @@ jobs:
   wheel-py3_14-cuda12_6-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -6442,7 +6442,7 @@ jobs:
   wheel-py3_14-cuda12_8-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -6680,7 +6680,7 @@ jobs:
   wheel-py3_14-cuda13_0-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -6918,7 +6918,7 @@ jobs:
   wheel-py3_14-xpu-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -7154,7 +7154,7 @@ jobs:
   wheel-py3_14t-cpu-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -7389,7 +7389,7 @@ jobs:
   wheel-py3_14t-cuda12_6-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -7627,7 +7627,7 @@ jobs:
   wheel-py3_14t-cuda12_8-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -7865,7 +7865,7 @@ jobs:
   wheel-py3_14t-cuda13_0-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch
@@ -8103,7 +8103,7 @@ jobs:
   wheel-py3_14t-xpu-build:
     if: ${{ github.repository_owner == 'pytorch' }}
     needs: get-label-type
-    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.12xlarge"
+    runs-on: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge"
     timeout-minutes: 360
     env:
       PYTORCH_ROOT: ${{ github.workspace }}/pytorch

.github/workflows/inductor-periodic.yml

@@ -88,6 +88,7 @@ jobs:
     with:
       build-environment: linux-jammy-rocm-py3_10
       docker-image-name: ci-image:pytorch-linux-jammy-rocm-n-py3-benchmarks
+      sync-tag: rocm-build
       test-matrix: |
         { include: [
           { config: "dynamo_eager_torchbench", shard: 1, num_shards: 2, runner: "linux.rocm.gpu.gfx942.1" },

.github/workflows/periodic.yml

@@ -147,16 +147,15 @@ jobs:
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
       build-environment: linux-jammy-cuda12.8-py3.10-gcc9-debug
       docker-image-name: ci-image:pytorch-linux-jammy-cuda12.8-cudnn9-py3-gcc9
-      cuda-arch-list: 8.9
       test-matrix: |
         { include: [
-          { config: "default", shard: 1, num_shards: 7, runner: "${{ needs.get-label-type.outputs.label-type }}linux.g6.4xlarge.experimental.nvidia.gpu", owners: ["oncall:debug-build"] },
-          { config: "default", shard: 2, num_shards: 7, runner: "${{ needs.get-label-type.outputs.label-type }}linux.g6.4xlarge.experimental.nvidia.gpu", owners: ["oncall:debug-build"] },
-          { config: "default", shard: 3, num_shards: 7, runner: "${{ needs.get-label-type.outputs.label-type }}linux.g6.4xlarge.experimental.nvidia.gpu", owners: ["oncall:debug-build"] },
-          { config: "default", shard: 4, num_shards: 7, runner: "${{ needs.get-label-type.outputs.label-type }}linux.g6.4xlarge.experimental.nvidia.gpu", owners: ["oncall:debug-build"] },
-          { config: "default", shard: 5, num_shards: 7, runner: "${{ needs.get-label-type.outputs.label-type }}linux.g6.4xlarge.experimental.nvidia.gpu", owners: ["oncall:debug-build"] },
-          { config: "default", shard: 6, num_shards: 7, runner: "${{ needs.get-label-type.outputs.label-type }}linux.g6.4xlarge.experimental.nvidia.gpu", owners: ["oncall:debug-build"] },
-          { config: "default", shard: 7, num_shards: 7, runner: "${{ needs.get-label-type.outputs.label-type }}linux.g6.4xlarge.experimental.nvidia.gpu", owners: ["oncall:debug-build"] },
+          { config: "default", shard: 1, num_shards: 7, runner: "${{ needs.get-label-type.outputs.label-type }}linux.4xlarge.nvidia.gpu", owners: ["oncall:debug-build"] },
+          { config: "default", shard: 2, num_shards: 7, runner: "${{ needs.get-label-type.outputs.label-type }}linux.4xlarge.nvidia.gpu", owners: ["oncall:debug-build"] },
+          { config: "default", shard: 3, num_shards: 7, runner: "${{ needs.get-label-type.outputs.label-type }}linux.4xlarge.nvidia.gpu", owners: ["oncall:debug-build"] },
+          { config: "default", shard: 4, num_shards: 7, runner: "${{ needs.get-label-type.outputs.label-type }}linux.4xlarge.nvidia.gpu", owners: ["oncall:debug-build"] },
+          { config: "default", shard: 5, num_shards: 7, runner: "${{ needs.get-label-type.outputs.label-type }}linux.4xlarge.nvidia.gpu", owners: ["oncall:debug-build"] },
+          { config: "default", shard: 6, num_shards: 7, runner: "${{ needs.get-label-type.outputs.label-type }}linux.4xlarge.nvidia.gpu", owners: ["oncall:debug-build"] },
+          { config: "default", shard: 7, num_shards: 7, runner: "${{ needs.get-label-type.outputs.label-type }}linux.4xlarge.nvidia.gpu", owners: ["oncall:debug-build"] },
         ]}
     secrets: inherit
 

.github/workflows/pull.yml

@@ -347,8 +347,7 @@ jobs:
     uses: ./.github/workflows/_linux-build.yml
     needs: get-label-type
     with:
-      # This should sync with the build in xpu.yml but xpu uses a larger runner
-      # sync-tag: linux-xpu-n-build
+      sync-tag: linux-xpu-n-build
       runner_prefix: ${{ needs.get-label-type.outputs.label-type }}
       build-environment: linux-jammy-xpu-n-py3.10
       docker-image-name: ci-image:pytorch-linux-jammy-xpu-n-py3

.github/workflows/rocm-mi300.yml

@@ -45,6 +45,7 @@ jobs:
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
       build-environment: linux-noble-rocm-py3.12-mi300
       docker-image-name: ci-image:pytorch-linux-noble-rocm-n-py3
+      sync-tag: rocm-build
       test-matrix: |
         { include: [
           { config: "default", shard: 1, num_shards: 6, runner: "linux.rocm.gpu.gfx942.1" },

.github/workflows/rocm-mi355.yml

@@ -42,6 +42,7 @@ jobs:
       runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
       build-environment: linux-noble-rocm-py3.12-mi355
       docker-image-name: ci-image:pytorch-linux-noble-rocm-n-py3
+      sync-tag: rocm-build
       test-matrix: |
         { include: [
           { config: "default", shard: 1, num_shards: 6, runner: "linux.rocm.gpu.mi355.1" },

.github/workflows/rocm-navi31.yml

@@ -1,75 +0,0 @@
-name: rocm-navi31
-
-on:
-  push:
-    tags:
-      - ciflow/rocm-navi31/*
-  workflow_dispatch:
-  schedule:
-    # We have several schedules so jobs can check github.event.schedule to activate only for a fraction of the runs.
-    # Also run less frequently on weekends.
-    - cron: 45 */2 * * 1-5
-    - cron: 45 4,12 * * 0,6
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref_name }}-${{ github.ref_type == 'branch' && github.sha }}-${{ github.event_name == 'workflow_dispatch' }}-${{ github.event_name == 'schedule' }}
-  cancel-in-progress: true
-
-permissions: read-all
-
-jobs:
-  target-determination:
-    if: github.repository_owner == 'pytorch'
-    name: before-test
-    uses: ./.github/workflows/target_determination.yml
-    permissions:
-      id-token: write
-      contents: read
-
-  get-label-type:
-    name: get-label-type
-    uses: pytorch/pytorch/.github/workflows/_runner-determinator.yml@main
-    if: ${{ (github.event_name != 'schedule' || github.repository == 'pytorch/pytorch') && github.repository_owner == 'pytorch' }}
-    with:
-      triggering_actor: ${{ github.triggering_actor }}
-      issue_owner: ${{ github.event.pull_request.user.login || github.event.issue.user.login }}
-      curr_branch: ${{ github.head_ref || github.ref_name }}
-      curr_ref_type: ${{ github.ref_type }}
-
-  linux-jammy-rocm-py3_10-build:
-    if: ${{ (github.event_name != 'schedule' || github.repository == 'pytorch/pytorch') && github.repository_owner == 'pytorch' }}
-    name: linux-jammy-rocm-py3.10
-    uses: ./.github/workflows/_linux-build.yml
-    needs: get-label-type
-    with:
-      runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      build-environment: linux-jammy-rocm-py3.10
-      docker-image-name: ci-image:pytorch-linux-jammy-rocm-n-py3
-      sync-tag: rocm-build
-      test-matrix: |
-        { include: [
-          { config: "default", shard: 1, num_shards: 2, runner: "linux.rocm.gpu.gfx1100" },
-          { config: "default", shard: 2, num_shards: 2, runner: "linux.rocm.gpu.gfx1100" },
-        ]}
-    secrets: inherit
-
-  linux-jammy-rocm-py3_10-test:
-    permissions:
-      id-token: write
-      contents: read
-    name: linux-jammy-rocm-py3_10
-    uses: ./.github/workflows/_rocm-test.yml
-    needs:
-      - linux-jammy-rocm-py3_10-build
-      - target-determination
-    with:
-      build-environment: linux-jammy-rocm-py3.10
-      docker-image: ${{ needs.linux-jammy-rocm-py3_10-build.outputs.docker-image }}
-      test-matrix: ${{ needs.linux-jammy-rocm-py3_10-build.outputs.test-matrix }}
-      tests-to-include: >-
-         ${{ github.event_name == 'schedule' && 'test_nn test_torch test_cuda test_ops test_unary_ufuncs test_binary_ufuncs
-         test_autograd inductor/test_torchinductor inductor/test_kernel_benchmark
-         inductor/test_pad_mm inductor/test_benchmark_fusion inductor/test_aot_inductor
-         inductor/test_torchinductor inductor/test_decompose_mem_bound_mm
-         inductor/test_flex_attention inductor/test_max_autotune' || '' }}
-    secrets: inherit

.github/workflows/rocm.yml

@@ -26,23 +26,11 @@ jobs:
       id-token: write
       contents: read
 
-  get-label-type:
-    name: get-label-type
-    uses: pytorch/pytorch/.github/workflows/_runner-determinator.yml@main
-    if: ${{ (github.event_name != 'schedule' || github.repository == 'pytorch/pytorch') && github.repository_owner == 'pytorch' }}
-    with:
-      triggering_actor: ${{ github.triggering_actor }}
-      issue_owner: ${{ github.event.pull_request.user.login || github.event.issue.user.login }}
-      curr_branch: ${{ github.head_ref || github.ref_name }}
-      curr_ref_type: ${{ github.ref_type }}
-
   linux-jammy-rocm-py3_10-build:
     if: ${{ (github.event_name != 'schedule' || github.repository == 'pytorch/pytorch') && github.repository_owner == 'pytorch' }}
     name: linux-jammy-rocm-py3.10
     uses: ./.github/workflows/_linux-build.yml
-    needs: get-label-type
     with:
-      runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
       build-environment: linux-jammy-rocm-py3.10
       docker-image-name: ci-image:pytorch-linux-jammy-rocm-n-py3
       sync-tag: rocm-build
@@ -71,3 +59,29 @@ jobs:
       docker-image: ${{ needs.linux-jammy-rocm-py3_10-build.outputs.docker-image }}
       test-matrix: ${{ needs.linux-jammy-rocm-py3_10-build.outputs.test-matrix }}
     secrets: inherit
+
+  linux-jammy-rocm-py3_10-gfx1100-test:
+    if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/main' }}
+    permissions:
+      id-token: write
+      contents: read
+    name: linux-jammy-rocm-py3_10-gfx1100
+    uses: ./.github/workflows/_rocm-test.yml
+    needs:
+      - linux-jammy-rocm-py3_10-build
+      - target-determination
+    with:
+      build-environment: linux-jammy-rocm-py3.10
+      docker-image: ${{ needs.linux-jammy-rocm-py3_10-build.outputs.docker-image }}
+      test-matrix: |
+        { include: [
+          { config: "default", shard: 1, num_shards: 2, runner: "linux.rocm.gpu.gfx1100" },
+          { config: "default", shard: 2, num_shards: 2, runner: "linux.rocm.gpu.gfx1100" },
+        ]}
+      tests-to-include: >
+         test_nn test_torch test_cuda test_ops test_unary_ufuncs test_binary_ufuncs
+         test_autograd inductor/test_torchinductor inductor/test_kernel_benchmark
+         inductor/test_pad_mm inductor/test_benchmark_fusion inductor/test_aot_inductor
+         inductor/test_torchinductor inductor/test_decompose_mem_bound_mm
+         inductor/test_flex_attention inductor/test_max_autotune
+    secrets: inherit

.github/workflows/trunk-tagging.yml

@@ -58,10 +58,8 @@ jobs:
           else
             COMMIT_SHA="${{ github.sha }}"
           fi
-          {
-            echo "sha=${COMMIT_SHA}"
-            echo "tag_name=trunk/${COMMIT_SHA}"
-          } >> "${GITHUB_OUTPUT}"
+          echo "sha=${COMMIT_SHA}" >> "${GITHUB_OUTPUT}"
+          echo "tag_name=trunk/${COMMIT_SHA}" >> "${GITHUB_OUTPUT}"
 
       - name: Validate commit SHA
         run: |
@@ -89,7 +87,7 @@ jobs:
             echo "✅ Commit ${COMMIT_SHA} is valid (automatic push trigger)"
           fi
 
-      - name: Create and push tag(s) with retry
+      - name: Create and push tag with retry
         id: check_tag
         env:
           TAG_NAME: ${{ steps.commit.outputs.tag_name }}
@@ -114,23 +112,14 @@ jobs:
             return 1
           }
 
-          # Counters for summary reporting
-          created_count=0
-          skipped_count=0
-          failed_count=0
+          # Exit early if tag already exists
+          if check_tag_exists; then
+            echo "✅ Tag already exists - no action needed"
+            echo "exists=true" >> "${GITHUB_OUTPUT}"
+            exit 0
+          fi
 
-          # Always write outputs once on exit
-          finish() {
-            set +e
-            if [ -n "${GITHUB_OUTPUT:-}" ]; then
-              {
-                echo "created_count=${created_count}"
-                echo "skipped_count=${skipped_count}"
-                echo "failed_count=${failed_count}"
-              } >> "${GITHUB_OUTPUT}"
-            fi
-          }
-          trap finish EXIT
+          echo "Tag ${TAG_NAME} does not exist, proceeding with creation"
 
           # Retry configuration
           MAX_RETRIES=5
@@ -205,111 +194,31 @@ jobs:
             }
           }
 
-          # New behavior for push events: enumerate commits in the push and tag each one.
-          # For workflow_dispatch, retain existing single-SHA behavior.
-
-          # Always fetch tags once up front to improve idempotency in loops
-          git fetch origin --tags --quiet || true
-
-          if [ "${{ github.event_name }}" = "push" ]; then
-            BEFORE_SHA="${{ github.event.before }}"
-            AFTER_SHA="${{ github.sha }}"  # same as event.after
-
-            # List commits introduced by this push (old..new), oldest first for stable ordering
-            commits_file="$(mktemp)"
-            git rev-list --reverse "${BEFORE_SHA}..${AFTER_SHA}" > "${commits_file}"
-
-            if [ ! -s "${commits_file}" ]; then
-              echo "No new commits found between ${BEFORE_SHA}..${AFTER_SHA}; nothing to tag."
-              rm -f "${commits_file}"
-              exit 0
-            fi
-
-            commit_count="$(wc -l < "${commits_file}" | tr -d ' ')"
-            echo "Found ${commit_count} commit(s) to tag for push:"
-            while IFS= read -r sha; do
-              printf '  %s\n' "${sha}"
-            done < "${commits_file}"
-
-            while IFS= read -r sha; do
-              TAG_NAME="trunk/${sha}"
-              COMMIT_SHA="${sha}"
-
-              # If tag already exists locally or remotely, skip (idempotent)
-              if check_tag_exists; then
-                echo "✅ Tag ${TAG_NAME} already exists - skipping"
-                skipped_count=$((skipped_count + 1))
-                continue
-              fi
-
-              echo "Tag ${TAG_NAME} does not exist, proceeding with creation"
-
-              if retry_with_backoff "tag_with_retry" "Creating tag ${TAG_NAME} for commit ${COMMIT_SHA}"; then
-                created_count=$((created_count + 1))
-              else
-                echo "Tag creation failed after all retry attempts for ${TAG_NAME}"
-                failed_count=$((failed_count + 1))
-              fi
-            done < "${commits_file}"
-
-            rm -f "${commits_file}"
-
-            if [ "${failed_count}" -gt 0 ]; then
-              exit 1
-            fi
+          # Execute with retry
+          if retry_with_backoff "tag_with_retry" "Creating tag ${TAG_NAME} for commit ${COMMIT_SHA}"; then
+            echo "exists=false" >> "${GITHUB_OUTPUT}"
             exit 0
           else
-            # workflow_dispatch path (single SHA tagging preserved)
-
-            # Exit early if tag already exists
-            if check_tag_exists; then
-              echo "✅ Tag already exists - no action needed"
-              skipped_count=1
-              exit 0
-            fi
-
-            echo "Tag ${TAG_NAME} does not exist, proceeding with creation"
-
-            if retry_with_backoff "tag_with_retry" "Creating tag ${TAG_NAME} for commit ${COMMIT_SHA}"; then
-              created_count=1
-              exit 0
-            else
-              echo "Tag creation failed after all retry attempts"
-              failed_count=1
-              exit 1
-            fi
+            echo "Tag creation failed after all retry attempts"
+            exit 1
           fi
 
       - name: Tag creation summary
         if: always()
         run: |
-          if [ "${{ github.event_name }}" = "push" ]; then
-            echo "Trigger: push on main"
-            echo "Created: ${{ steps.check_tag.outputs.created_count }}"
-            echo "Skipped (already existed): ${{ steps.check_tag.outputs.skipped_count }}"
-            echo "Failed: ${{ steps.check_tag.outputs.failed_count }}"
-            if [ "${{ steps.check_tag.outputs.failed_count }}" = "0" ]; then
-              echo "✅ Completed tagging for push range ${{ github.event.before }}..${{ github.sha }}"
-            else
-              echo "❌ Some tags failed to create for push range ${{ github.event.before }}..${{ github.sha }}"
-            fi
+          if [ "${{ steps.check_tag.outputs.exists }}" = "true" ]; then
+            echo "✅ Tag ${{ steps.commit.outputs.tag_name }} already existed - no action needed"
+          elif [ "${{ job.status }}" = "success" ]; then
+            echo "✅ Successfully created tag ${{ steps.commit.outputs.tag_name }} for commit ${{ steps.commit.outputs.sha }}"
           else
-            if [ "${{ steps.check_tag.outputs.failed_count }}" = "0" ]; then
-              if [ "${{ steps.check_tag.outputs.created_count }}" = "0" ]; then
-                echo "✅ Tag ${{ steps.commit.outputs.tag_name }} already existed - no action needed"
-              else
-                echo "✅ Successfully created tag ${{ steps.commit.outputs.tag_name }} for commit ${{ steps.commit.outputs.sha }}"
-              fi
-            else
-              echo "❌ Failed to create tag ${{ steps.commit.outputs.tag_name }} for commit ${{ steps.commit.outputs.sha }}"
-            fi
-
-            echo ""
-            echo "Tag details:"
-            echo "  Name: ${{ steps.commit.outputs.tag_name }}"
-            echo "  Commit: ${{ steps.commit.outputs.sha }}"
-            echo "  Trigger: ${{ github.event_name }}"
-            if [ -n "${{ github.event.inputs.commit_sha }}" ]; then
-              echo "  Manual commit: ${{ github.event.inputs.commit_sha }}"
-            fi
+            echo "❌ Failed to create tag ${{ steps.commit.outputs.tag_name }} for commit ${{ steps.commit.outputs.sha }}"
+          fi
+
+          echo ""
+          echo "Tag details:"
+          echo "  Name: ${{ steps.commit.outputs.tag_name }}"
+          echo "  Commit: ${{ steps.commit.outputs.sha }}"
+          echo "  Trigger: ${{ github.event_name }}"
+          if [ -n "${{ github.event.inputs.commit_sha }}" ]; then
+            echo "  Manual commit: ${{ github.event.inputs.commit_sha }}"
           fi

.github/workflows/trunk.yml

@@ -190,40 +190,6 @@ jobs:
       runner: "${{ needs.get-label-type.outputs.label-type }}windows.4xlarge.nonephemeral"
     secrets: inherit
 
-  linux-jammy-rocm-py3_10-build:
-    if: ${{ startsWith(github.event.ref, 'refs/tags/ciflow/trunk') }}
-    name: linux-jammy-rocm-py3.10
-    uses: ./.github/workflows/_linux-build.yml
-    needs: get-label-type
-    with:
-      runner_prefix: "${{ needs.get-label-type.outputs.label-type }}"
-      build-environment: linux-jammy-rocm-py3.10
-      docker-image-name: ci-image:pytorch-linux-jammy-rocm-n-py3
-      sync-tag: rocm-build
-      test-matrix: |
-        { include: [
-          { config: "default", shard: 1, num_shards: 2, runner: "linux.rocm.gpu.gfx942.1" },
-          { config: "default", shard: 2, num_shards: 2, runner: "linux.rocm.gpu.gfx942.1" },
-        ]}
-    secrets: inherit
-
-  linux-jammy-rocm-py3_10-test:
-    if: ${{ startsWith(github.event.ref, 'refs/tags/ciflow/trunk') }}
-    permissions:
-      id-token: write
-      contents: read
-    name: linux-jammy-rocm-py3.10
-    uses: ./.github/workflows/_rocm-test.yml
-    needs:
-      - linux-jammy-rocm-py3_10-build
-      - target-determination
-    with:
-      build-environment: linux-jammy-rocm-py3.10
-      docker-image: ${{ needs.linux-jammy-rocm-py3_10-build.outputs.docker-image }}
-      test-matrix: ${{ needs.linux-jammy-rocm-py3_10-build.outputs.test-matrix }}
-      tests-to-include: "test_nn test_torch test_cuda test_ops test_unary_ufuncs test_binary_ufuncs test_autograd inductor/test_torchinductor"
-    secrets: inherit
-
   inductor-build:
     name: inductor-build
     uses: ./.github/workflows/_linux-build.yml

.gitignore

@@ -374,7 +374,6 @@ third_party/ruy/
 third_party/glog/
 
 # Virtualenv
-.venv/
 venv/
 
 # Log files

.lintrunner.toml

@@ -1138,8 +1138,11 @@ command = [
 [[linter]]
 code = 'WORKFLOWSYNC'
 include_patterns = [
-    '.github/workflows/*.yml',
-    '.github/workflows/*.yaml',
+    '.github/workflows/pull.yml',
+    '.github/workflows/trunk.yml',
+    '.github/workflows/periodic.yml',
+    '.github/workflows/mac-mps.yml',
+    '.github/workflows/slow.yml',
 ]
 command = [
     'python3',

CODEOWNERS

@@ -201,17 +201,3 @@ torch/backends/cudnn/ @eqy @syed-ahmed @Aidyn-A
 /torch/csrc/stable/ @janeyx99 @mikaylagawarecki
 /torch/headeronly/ @janeyx99
 /torch/header_only_apis.txt @janeyx99
-
-# FlexAttention
-/torch/nn/attention/flex_attention.py @drisspg
-/torch/_higher_order_ops/flex_attention.py @drisspg
-/torch/_inductor/kernel/flex/ @drisspg
-/torch/_inductor/codegen/cpp_flex_attention_template.py @drisspg
-/test/inductor/test_flex_attention.py @drisspg
-/test/inductor/test_flex_decoding.py @drisspg
-
-# Low Precision GEMMs
-/aten/src/ATen/native/cuda/Blas.cpp @drisspg @slayton58
-/aten/src/ATen/cuda/CUDABlas.cpp @drisspg @slayton58
-/aten/src/ATen/cuda/CUDABlas.h @drisspg @slayton58
-/test/test_scaled_matmul_cuda.py @drisspg @slayton58

Dockerfile

@@ -39,7 +39,7 @@ RUN chmod +x ~/miniconda.sh && \
     bash ~/miniconda.sh -b -p /opt/conda && \
     rm ~/miniconda.sh && \
     /opt/conda/bin/conda install -y python=${PYTHON_VERSION} cmake conda-build pyyaml numpy ipython && \
-    /opt/conda/bin/python -mpip install -r requirements.txt && \
+    /opt/conda/bin/python -m pip install -r requirements.txt && \
     /opt/conda/bin/conda clean -ya
 
 FROM dev-base as submodule-update

aten/src/ATen/CMakeLists.txt

@@ -289,15 +289,14 @@ IF(USE_FBGEMM_GENAI)
 
     set_target_properties(fbgemm_genai PROPERTIES POSITION_INDEPENDENT_CODE ON)
 
-    set(fbgemm_genai_cuh
+    set(fbgemm_genai_mx8mx8bf16_grouped
       "${FBGEMM_GENAI_SRCS}/cutlass_extensions/mx8mx8bf16_grouped/"
-      "${FBGEMM_GENAI_SRCS}/"
     )
 
     target_include_directories(fbgemm_genai PRIVATE
       ${FBGEMM_THIRD_PARTY}/cutlass/include
       ${FBGEMM_THIRD_PARTY}/cutlass/tools/util/include
-      ${fbgemm_genai_cuh}
+      ${fbgemm_genai_mx8mx8bf16_grouped}
       ${FBGEMM_GENAI_SRCS}/common/include/   # includes fbgemm_gpu/quantize/utils.h, fbgemm_gpu/quantize/tuning_cache.hpp
       ${FBGEMM_GENAI_SRCS}/include/          # includes fbgemm_gpu/torch_ops.h
     )
@@ -314,14 +313,13 @@ IF(USE_FBGEMM_GENAI)
 
     # Add additional HIPCC compiler flags for performance
     set(FBGEMM_GENAI_EXTRA_HIPCC_FLAGS
+      -mllvm
+      -amdgpu-coerce-illegal-types=1
       -mllvm
       -enable-post-misched=0
       -mllvm
       -greedy-reverse-local-assignment=1
       -fhip-new-launch-api)
-    if(DEFINED ROCM_VERSION_DEV AND ROCM_VERSION_DEV VERSION_LESS "7.2.0")
-        list(PREPEND FBGEMM_GENAI_EXTRA_HIPCC_FLAGS -mllvm -amdgpu-coerce-illegal-types=1)
-      endif()
 
     # Only compile for gfx942 for now.
     # This is rather hacky, I could not figure out a clean solution :(

aten/src/ATen/Context.h

@@ -19,7 +19,6 @@
 #include <ATen/detail/MPSHooksInterface.h>
 #include <ATen/detail/MTIAHooksInterface.h>
 #include <ATen/detail/PrivateUse1HooksInterface.h>
-#include <ATen/detail/XLAHooksInterface.h>
 #include <ATen/detail/XPUHooksInterface.h>
 #include <c10/core/QEngine.h>
 #include <c10/core/impl/DeviceGuardImplInterface.h>
@@ -89,8 +88,6 @@ class TORCH_API Context {
       return at::detail::getHIPHooks();
     } else if (opt_device_type == at::kHPU) {
       return at::detail::getHPUHooks();
-    } else if (opt_device_type == at::kXLA) {
-      return at::detail::getXLAHooks();
     } else {
       TORCH_CHECK(
           false,
@@ -199,7 +196,7 @@ class TORCH_API Context {
     return c10::impl::hasDeviceGuardImpl(c10::DeviceType::IPU);
   }
   static bool hasXLA() {
-    return detail::getXLAHooks().hasXLA();
+    return c10::impl::hasDeviceGuardImpl(c10::DeviceType::XLA);
   }
   static bool hasXPU() {
     return detail::getXPUHooks().hasXPU();

aten/src/ATen/core/CachingHostAllocator.h

@@ -39,7 +39,7 @@ struct HostBlock {
 };
 
 template <typename B>
-struct alignas(hardware_destructive_interference_size) FreeBlockList {
+struct alignas(64) FreeBlockList {
   std::mutex mutex_;
   std::deque<B*> list_;
 };
@@ -122,7 +122,7 @@ struct TORCH_API HostStats {
 // Struct containing memory allocator summary statistics for host, as they
 // are staged for reporting. This is a temporary struct that is used to
 // avoid locking the allocator while collecting stats.
-struct alignas(hardware_destructive_interference_size) HostStatsStaged {
+struct alignas(64) HostStatsStaged {
   std::mutex timing_mutex_;
   // COUNT: total allocations (active + free)
   // LOCK: access to this stat is protected by the allocator's blocks_mutex_
@@ -669,7 +669,7 @@ struct CachingHostAllocatorImpl {
     TORCH_CHECK_NOT_IMPLEMENTED(false, "Not implemented for query_event");
   }
 
-  alignas(hardware_destructive_interference_size) std::mutex blocks_mutex_;
+  alignas(64) std::mutex blocks_mutex_;
   ska::flat_hash_set<B*> blocks_; // block list
   ska::flat_hash_map<void*, B*> ptr_to_block_;
 
@@ -677,17 +677,17 @@ struct CachingHostAllocatorImpl {
   // size. This allows us to quickly find a free block of the right size.
   // We use deque to store per size free list and guard the list with its own
   // mutex.
-  alignas(hardware_destructive_interference_size) std::vector<FreeBlockList<B>> free_list_ =
+  alignas(64) std::vector<FreeBlockList<B>> free_list_ =
       std::vector<FreeBlockList<B>>(MAX_SIZE_INDEX);
 
-  alignas(hardware_destructive_interference_size) std::mutex events_mutex_;
+  alignas(64) std::mutex events_mutex_;
   std::deque<std::pair<E, B*>> events_; // event queue paired with block
 
   // Indicates whether the object is active.
   // Set to false in the destructor to signal background threads to stop.
   std::atomic<bool> active_{true};
 protected:
-  alignas(hardware_destructive_interference_size) HostStatsStaged stats_;
+  alignas(64) HostStatsStaged stats_;
 };
 
 struct TORCH_API HostAllocator : public at::Allocator {

aten/src/ATen/core/Generator.h

@@ -59,7 +59,9 @@ struct TORCH_API Generator {
 
   explicit Generator(c10::intrusive_ptr<c10::GeneratorImpl> gen_impl)
    : impl_(std::move(gen_impl)) {
-    TORCH_CHECK(impl_.get(), "GeneratorImpl with nullptr is not supported");
+    if (impl_.get() == nullptr) {
+      throw std::runtime_error("GeneratorImpl with nullptr is not supported");
+    }
   }
 
   bool operator==(const Generator& rhs) const {

aten/src/ATen/core/TensorBase.h

@@ -111,7 +111,9 @@ class TORCH_API TensorBase {
   explicit TensorBase(
       c10::intrusive_ptr<TensorImpl, UndefinedTensorImpl> tensor_impl)
       : impl_(std::move(tensor_impl)) {
-    TORCH_CHECK(impl_.get(), "TensorImpl with nullptr is not supported");
+    if (impl_.get() == nullptr) {
+      throw std::runtime_error("TensorImpl with nullptr is not supported");
+    }
   }
   TensorBase(const TensorBase&) = default;
   TensorBase(TensorBase&&) noexcept = default;

aten/src/ATen/core/interned_strings.cpp

@@ -68,7 +68,11 @@ Symbol InternedStrings::_symbol(const std::string& s) {
     return it->second;
 
   auto pos = s.find("::");
-  TORCH_CHECK(pos != std::string::npos, "all symbols must have a namespace, <namespace>::<string>, but found: ", s);
+  if (pos == std::string::npos) {
+    std::stringstream ss;
+    ss << "all symbols must have a namespace, <namespace>::<string>, but found: " << s;
+    throw std::runtime_error(ss.str());
+  }
   Symbol ns = _symbol("namespaces::" + s.substr(0, pos));
 
   Symbol sym(sym_to_info_.size());
@@ -117,7 +121,12 @@ std::string Symbol::domainString() const {
 }
 
 Symbol Symbol::fromDomainAndUnqualString(const std::string & d, const std::string & s) {
-  TORCH_CHECK(d.compare(0, domain_prefix().size(), domain_prefix()) == 0, "Symbol: domain string is expected to be prefixed with '", domain_prefix(), "', e.g. 'org.pytorch.aten'");
+  if (d.compare(0, domain_prefix().size(), domain_prefix()) != 0) {
+    std::ostringstream ss;
+    ss << "Symbol: domain string is expected to be prefixed with '"
+       << domain_prefix() << "', e.g. 'org.pytorch.aten'";
+    throw std::runtime_error(ss.str());
+  }
   std::string qualString = d.substr(domain_prefix().size()) + "::" + s;
   return fromQualString(qualString);
 }

aten/src/ATen/core/ivalue.cpp

@@ -7,7 +7,6 @@
 #include <ATen/core/jit_type.h>
 #include <ATen/core/stack.h>
 #include <ATen/core/type_factory.h>
-#include <c10/util/Exception.h>
 #include <c10/util/StringUtil.h>
 #include <c10/util/hash.h>
 #include <c10/util/irange.h>
@@ -413,7 +412,7 @@ size_t IValue::hash(const IValue& v) {
     case Tag::Enum:
     case Tag::Stream:
     case Tag::Uninitialized:
-      TORCH_CHECK(false,
+      throw std::runtime_error(
           "unhashable type: '" + v.type()->repr_str() + "'");
   }
   // the above switch should be exhaustive

aten/src/ATen/core/jit_type.h

@@ -8,7 +8,6 @@
 #include <ATen/core/type_factory.h>
 #include <ATen/core/qualified_name.h>
 #include <c10/util/TypeList.h>
-#include <c10/util/Exception.h>
 #include <optional>
 #include <c10/core/SymFloat.h>
 #include <c10/core/SymBool.h>
@@ -117,8 +116,10 @@ struct SingleElementType : public SharedType {
 
  protected:
   SingleElementType(TypePtr elem) : SharedType(Kind), elem(std::move(elem)) {
-    TORCH_CHECK(this->elem, c10::str(
+    if (!this->elem) {
+      throw std::runtime_error(c10::str(
             "Can not create ", typeKindToString(Kind), " with None type"));
+    }
   }
 
  private:
@@ -415,12 +416,16 @@ struct TORCH_API SymbolicShape {
   }
 
   ShapeSymbol operator[](size_t i) const {
-    TORCH_CHECK(dims_, "Rank isn't fixed");
+    if (!dims_) {
+      throw std::runtime_error("Rank isn't fixed");
+    }
     return (*dims_).at(i);
   }
 
   ShapeSymbol at(size_t i) const {
-    TORCH_CHECK(dims_, "Rank isn't fixed");
+    if (!dims_) {
+      throw std::runtime_error("Rank isn't fixed");
+    }
     return (*dims_).at(i);
   }
 
@@ -515,7 +520,9 @@ struct VaryingShape {
   }
 
   const std::optional<T> &operator[](size_t i) const {
-    TORCH_CHECK(dims_, "Rank isn't fixed");
+    if (!dims_) {
+      throw std::runtime_error("Rank isn't fixed");
+    }
     return (*dims_).at(i);
   }
 
@@ -950,7 +957,9 @@ struct TORCH_API DictType : public SharedType {
 
   TypePtr createWithContained(
       std::vector<TypePtr> contained_types) const override {
-    TORCH_CHECK(contained_types.size() == 2, "Expected 2 contained types");
+    if (contained_types.size() != 2) {
+      throw std::runtime_error("Expected 2 contained types");
+    }
     return create(std::move(contained_types.at(0)), std::move(contained_types.at(1)));
   }
 

aten/src/ATen/core/type.cpp

@@ -8,7 +8,6 @@
 #include <ATen/core/jit_type.h>
 #include <c10/macros/Macros.h>
 #include <c10/util/env.h>
-#include <c10/util/Exception.h>
 #include <c10/util/flat_hash_map.h>
 #include <c10/util/irange.h>
 #include <array>
@@ -827,7 +826,9 @@ TupleType::TupleType(
     : NamedType(TypeKind::TupleType, std::move(name)),
       elements_(std::move(elements)),
       has_free_variables_(std::any_of(elements_.begin(), elements_.end(), [](const TypePtr& v) {
-        TORCH_CHECK(v, "Can not create tuple with None type");
+        if (!v) {
+          throw std::runtime_error("Can not create tuple with None type");
+        }
         return v->hasFreeVariables();
       })), schema_(std::move(schema)) {
 

aten/src/ATen/cpu/vec/vec128/vec128.h

@@ -6,11 +6,9 @@
 #ifdef __aarch64__
 #if !defined(CPU_CAPABILITY_SVE)
 #include <ATen/cpu/vec/vec128/vec128_bfloat16_neon.h>
-#include <ATen/cpu/vec/vec128/vec128_double_neon.h>
 #include <ATen/cpu/vec/vec128/vec128_float_neon.h>
 #include <ATen/cpu/vec/vec128/vec128_half_neon.h>
 #include <ATen/cpu/vec/vec128/vec128_int_aarch64.h>
-#include <ATen/cpu/vec/vec128/vec128_uint_aarch64.h>
 #endif
 
 #include <ATen/cpu/vec/vec128/vec128_convert.h>

aten/src/ATen/cpu/vec/vec128/vec128_bfloat16_neon.h

@@ -354,47 +354,9 @@ class Vectorized<c10::BFloat16> : public Vectorized16<
 
   DEFINE_UNARY_ELEMENTWISE_FUNC_VIA_FLOAT_METHOD(abs)
   Vectorized frac() const;
+  DEFINE_UNARY_ELEMENTWISE_FUNC_VIA_FLOAT_METHOD(neg)
   DEFINE_UNARY_ELEMENTWISE_FUNC_VIA_FLOAT_METHOD(trunc)
   DEFINE_UNARY_ELEMENTWISE_FUNC_VIA_FLOAT_METHOD(sqrt)
-
-#ifdef __ARM_FEATURE_BF16
-  Vectorized<c10::BFloat16> neg() const {
-    return -values;
-  }
-  Vectorized<c10::BFloat16> reciprocal() const {
-    return 1.0f / values;
-  }
-  Vectorized<c10::BFloat16> operator==(
-      const Vectorized<c10::BFloat16>& other) const {
-    return values == other.values;
-  }
-
-  Vectorized<c10::BFloat16> operator!=(
-      const Vectorized<c10::BFloat16>& other) const {
-    return values != other.values;
-  }
-
-  Vectorized<c10::BFloat16> operator<(
-      const Vectorized<c10::BFloat16>& other) const {
-    return values < other.values;
-  }
-
-  Vectorized<c10::BFloat16> operator<=(
-      const Vectorized<c10::BFloat16>& other) const {
-    return values <= other.values;
-  }
-
-  Vectorized<c10::BFloat16> operator>(
-      const Vectorized<c10::BFloat16>& other) const {
-    return values > other.values;
-  }
-
-  Vectorized<c10::BFloat16> operator>=(
-      const Vectorized<c10::BFloat16>& other) const {
-    return values >= other.values;
-  }
-#else
-  DEFINE_UNARY_ELEMENTWISE_FUNC_VIA_FLOAT_METHOD(neg)
   DEFINE_UNARY_ELEMENTWISE_FUNC_VIA_FLOAT_METHOD(reciprocal)
   DEFINE_BINARY_COMPARISON_OPERATOR_VIA_FLOAT_METHOD(operator==)
   DEFINE_BINARY_COMPARISON_OPERATOR_VIA_FLOAT_METHOD(operator!=)
@@ -402,7 +364,6 @@ class Vectorized<c10::BFloat16> : public Vectorized16<
   DEFINE_BINARY_COMPARISON_OPERATOR_VIA_FLOAT_METHOD(operator<=)
   DEFINE_BINARY_COMPARISON_OPERATOR_VIA_FLOAT_METHOD(operator>)
   DEFINE_BINARY_COMPARISON_OPERATOR_VIA_FLOAT_METHOD(operator>=)
-#endif
 
 #undef DEFINE_UNARY_ELEMENTWISE_FUNC_VIA_FLOAT_METHOD
 #undef DEFINE_BINARY_ELEMENTWISE_FUNC_VIA_FLOAT_METHOD
@@ -451,52 +412,28 @@ template <>
 Vectorized<c10::BFloat16> inline operator+(
     const Vectorized<c10::BFloat16>& a,
     const Vectorized<c10::BFloat16>& b) {
-#ifdef __ARM_FEATURE_BF16
-  bfloat16x8_t x = a;
-  bfloat16x8_t y = b;
-  return x + y;
-#else
   return binary_operator_via_float(std::plus<Vectorized<float>>(), a, b);
-#endif
 }
 
 template <>
 Vectorized<c10::BFloat16> inline operator-(
     const Vectorized<c10::BFloat16>& a,
     const Vectorized<c10::BFloat16>& b) {
-#ifdef __ARM_FEATURE_BF16
-  bfloat16x8_t x = a;
-  bfloat16x8_t y = b;
-  return x - y;
-#else
   return binary_operator_via_float(std::minus<Vectorized<float>>(), a, b);
-#endif
 }
 
 template <>
 Vectorized<c10::BFloat16> inline operator*(
     const Vectorized<c10::BFloat16>& a,
     const Vectorized<c10::BFloat16>& b) {
-#ifdef __ARM_FEATURE_BF16
-  bfloat16x8_t x = a;
-  bfloat16x8_t y = b;
-  return x * y;
-#else
   return binary_operator_via_float(std::multiplies<Vectorized<float>>(), a, b);
-#endif
 }
 
 template <>
 Vectorized<c10::BFloat16> inline operator/(
     const Vectorized<c10::BFloat16>& a,
     const Vectorized<c10::BFloat16>& b) {
-#ifdef __ARM_FEATURE_BF16
-  bfloat16x8_t x = a;
-  bfloat16x8_t y = b;
-  return x / y;
-#else
   return binary_operator_via_float(std::divides<Vectorized<float>>(), a, b);
-#endif
 }
 
 // frac. Implement this here so we can use subtraction
@@ -607,19 +544,12 @@ Vectorized<c10::BFloat16> inline fmadd(
     const Vectorized<c10::BFloat16>& a,
     const Vectorized<c10::BFloat16>& b,
     const Vectorized<c10::BFloat16>& c) {
-#ifdef __ARM_FEATURE_BF16
-  bfloat16x8_t x = a;
-  bfloat16x8_t y = b;
-  bfloat16x8_t z = c;
-  return x * y + z;
-#else
   // NOTE [BF16 FMA]: There isn't an FMA that accumulates into BF16!  Also,
   // vbfmlalbq_f32 and vbfmlaltq_f32 take the even and odd-numbered
   // elements, not the bottom and top half, so they don't seem
   // particularly useful here. Ideally we would include dot product in
   // the Vectorized interface...
   return a * b + c;
-#endif
 }
 
 template <>
@@ -627,15 +557,8 @@ Vectorized<c10::BFloat16> inline fnmadd(
     const Vectorized<c10::BFloat16>& a,
     const Vectorized<c10::BFloat16>& b,
     const Vectorized<c10::BFloat16>& c) {
-#ifdef __ARM_FEATURE_BF16
-  bfloat16x8_t x = a;
-  bfloat16x8_t y = b;
-  bfloat16x8_t z = c;
-  return (-x) * y + z;
-#else
   // See NOTE [BF16 FMA] above.
   return -a * b + c;
-#endif
 }
 
 template <>
@@ -643,15 +566,8 @@ Vectorized<c10::BFloat16> inline fmsub(
     const Vectorized<c10::BFloat16>& a,
     const Vectorized<c10::BFloat16>& b,
     const Vectorized<c10::BFloat16>& c) {
-#ifdef __ARM_FEATURE_BF16
-  bfloat16x8_t x = a;
-  bfloat16x8_t y = b;
-  bfloat16x8_t z = c;
-  return x * y - z;
-#else
   // See NOTE [BF16 FMA] above.
   return a * b - c;
-#endif
 }
 
 template <>
@@ -659,15 +575,8 @@ Vectorized<c10::BFloat16> inline fnmsub(
     const Vectorized<c10::BFloat16>& a,
     const Vectorized<c10::BFloat16>& b,
     const Vectorized<c10::BFloat16>& c) {
-#ifdef __ARM_FEATURE_BF16
-  bfloat16x8_t x = a;
-  bfloat16x8_t y = b;
-  bfloat16x8_t z = c;
-  return (-x) * y - z;
-#else
   // See NOTE [BF16 FMA] above.
   return -a * b - c;
-#endif
 }
 
 #endif // !defined(C10_MOBILE) && defined(__aarch64__)

aten/src/ATen/cpu/vec/vec128/vec128_double_neon.h

@@ -1,586 +0,0 @@
-#pragma once
-
-#include <ATen/cpu/vec/intrinsics.h>
-#include <ATen/cpu/vec/vec_base.h>
-#include <c10/macros/Macros.h>
-#include <c10/util/irange.h>
-#include <cmath>
-
-namespace at::vec {
-// Note [CPU_CAPABILITY namespace]
-// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-// This header, and all of its subheaders, will be compiled with
-// different architecture flags for each supported set of vector
-// intrinsics. So we need to make sure they aren't inadvertently
-// linked together. We do this by declaring objects in an `inline
-// namespace` which changes the name mangling, but can still be
-// accessed as `at::vec`.
-inline namespace CPU_CAPABILITY {
-
-template <>
-struct is_vec_specialized_for<double> : std::bool_constant<true> {};
-
-template <>
-class Vectorized<double> {
- private:
-  float64x2_t values;
-
- public:
-  using value_type = double;
-  using size_type = int;
-  static constexpr size_type size() {
-    return 2;
-  }
-  Vectorized() {
-    values = vdupq_n_f64(0.0);
-  }
-  Vectorized(float64x2_t v) : values(v) {}
-  Vectorized(double val) {
-    values = vdupq_n_f64(val);
-  }
-  template <
-      typename... Args,
-      typename = std::enable_if_t<(sizeof...(Args) == size())>>
-  Vectorized(Args... vals) {
-    __at_align__ double buffer[size()] = {vals...};
-    values = vld1q_f64(buffer);
-  }
-  operator float64x2_t() const {
-    return values;
-  }
-  template <int64_t mask>
-  static Vectorized<double> blend(
-      const Vectorized<double>& a,
-      const Vectorized<double>& b) {
-    // Build an array of flags: each bit of element is 1 if the corresponding
-    // bit in 'mask' is set, 0 otherwise.
-    uint64x2_t maskArray = {
-        (mask & 1ULL) ? 0xFFFFFFFFFFFFFFFF : 0,
-        (mask & 2ULL) ? 0xFFFFFFFFFFFFFFFF : 0};
-    // Use BSL to select elements from b where the mask is 1, else from a
-    return vbslq_f64(maskArray, b.values, a.values);
-  }
-  static Vectorized<double> blendv(
-      const Vectorized<double>& a,
-      const Vectorized<double>& b,
-      const Vectorized<double>& mask_) {
-    return vbslq_f64(vreinterpretq_u64_f64(mask_.values), b.values, a.values);
-  }
-  template <typename step_t>
-  static Vectorized<double> arange(
-      double base = 0.,
-      step_t step = static_cast<step_t>(1)) {
-    return {base, base + static_cast<double>(step)};
-  }
-  static inline Vectorized<double> set(
-      const Vectorized<double>& a,
-      const Vectorized<double>& b,
-      int64_t count = size()) {
-    if (count == 0) {
-      return a;
-    } else if (count >= 2) {
-      return b;
-    } else {
-      float64x2_t c = {b.values[0], a.values[1]};
-      return c;
-    }
-  }
-  static Vectorized<double> loadu(const void* ptr, int64_t count = size()) {
-    if (count == size()) {
-      return vld1q_f64(reinterpret_cast<const double*>(ptr));
-    } else if (count == 1) {
-      float64x1_t x = vld1_f64(reinterpret_cast<const double*>(ptr));
-      float64x1_t z = {0.0};
-      return vcombine_f64(x, z);
-    } else {
-      return vdupq_n_f64(0.0);
-    }
-  }
-  void store(void* ptr, int64_t count = size()) const {
-    if (count == size()) {
-      vst1q_f64(reinterpret_cast<double*>(ptr), values);
-    } else if (count == 1) {
-      vst1_f64(reinterpret_cast<double*>(ptr), vget_low_f64(values));
-    }
-  }
-  const double& operator[](int idx) const = delete;
-  double& operator[](int idx) = delete;
-  int64_t zero_mask() const {
-    // returns an integer mask where all zero elements are translated to 1-bit
-    // and others are translated to 0-bit
-    uint64x2_t cmpReg = vceqzq_f64(values);
-    uint64x2_t mask = {1, 2};
-    uint64x2_t res = vandq_u64(cmpReg, mask);
-    return res[0] | res[1];
-  }
-  Vectorized<double> isnan() const {
-    // NaN check
-    return vreinterpretq_f64_u32(
-        vmvnq_u32(vreinterpretq_u32_u64(vceqq_f64(values, values))));
-  }
-  bool has_inf_nan() const {
-    Vectorized<double> x = vsubq_f64(values, values);
-    float64x2_t r = x.isnan();
-    uint64x2_t u = vreinterpretq_u64_f64(r);
-    return u[0] | u[1];
-  }
-  Vectorized<double> map(double (*f)(double)) const {
-    float64x2_t result;
-    result[0] = f(values[0]);
-    result[1] = f(values[1]);
-    return result;
-  }
-  Vectorized<double> map2(
-      const Vectorized<double>& second,
-      double (*const f)(double, double)) const {
-    float64x2_t result;
-    result[0] = f(values[0], second.values[0]);
-    result[1] = f(values[1], second.values[1]);
-    return result;
-  }
-  Vectorized<double> abs() const {
-    return vabsq_f64(values);
-  }
-  Vectorized<double> angle() const {
-    auto zero = Vectorized<double>(0.0);
-    auto pi = Vectorized<double>(c10::pi<double>);
-    auto tmp = blendv(zero, pi, vreinterpretq_f64_u64(vcltzq_f64(values)));
-    return blendv(tmp, *this, isnan());
-  }
-  Vectorized<double> real() const {
-    return *this;
-  }
-  Vectorized<double> imag() const {
-    return Vectorized<double>(0.0);
-  }
-  Vectorized<double> conj() const {
-    return *this;
-  }
-  Vectorized<double> acos() const {
-    return USE_SLEEF(
-        Vectorized<double>(Sleef_acosd2_u10(values)), map(std::acos));
-  }
-  Vectorized<double> acosh() const {
-    return USE_SLEEF(
-        Vectorized<double>(Sleef_acoshd2_u10(values)), map(std::acosh));
-  }
-  Vectorized<double> asin() const {
-    return USE_SLEEF(
-        Vectorized<double>(Sleef_asind2_u10(values)), map(std::asin));
-  }
-  Vectorized<double> asinh() const {
-    return USE_SLEEF(
-        Vectorized<double>(Sleef_asinhd2_u10(values)), map(std::asinh));
-  }
-  Vectorized<double> atan() const {
-    return USE_SLEEF(
-        Vectorized<double>(Sleef_atand2_u10(values)), map(std::atan));
-  }
-  Vectorized<double> atanh() const {
-    return USE_SLEEF(
-        Vectorized<double>(Sleef_atanhd2_u10(values)), map(std::atanh));
-  }
-  Vectorized<double> atan2(const Vectorized<double>& b) const {USE_SLEEF(
-      { return Vectorized<double>(Sleef_atan2d2_u10(values, b)); },
-      {
-        __at_align__ double tmp[size()];
-        __at_align__ double tmp_b[size()];
-        store(tmp);
-        b.store(tmp_b);
-        for (int64_t i = 0; i < size(); i++) {
-          tmp[i] = std::atan2(tmp[i], tmp_b[i]);
-        }
-        return loadu(tmp);
-      })} Vectorized<double> copysign(const Vectorized<double>& sign) const {
-      USE_SLEEF(
-          { return Vectorized<double>(Sleef_copysignd2(values, sign)); },
-          {
-            __at_align__ double tmp[size()];
-            __at_align__ double tmp_sign[size()];
-            store(tmp);
-            sign.store(tmp_sign);
-            for (int64_t i = 0; i < size(); i++) {
-              tmp[i] = std::copysign(tmp[i], tmp_sign[i]);
-            }
-            return loadu(tmp);
-          })} Vectorized<double> erf() const {
-    return USE_SLEEF(
-        Vectorized<double>(Sleef_erfd2_u10(values)), map(std::erf));
-  }
-  Vectorized<double> erfc() const {
-    return USE_SLEEF(
-        Vectorized<double>(Sleef_erfcd2_u15(values)), map(std::erfc));
-  }
-  Vectorized<double> exp() const {
-    return USE_SLEEF(
-        Vectorized<double>(Sleef_expd2_u10(values)), map(std::exp));
-  }
-  Vectorized<double> exp2() const {
-    return USE_SLEEF(
-        Vectorized<double>(Sleef_exp2d2_u10(values)), map(std::exp2));
-  }
-  Vectorized<double> expm1() const {
-    return USE_SLEEF(
-        Vectorized<double>(Sleef_expm1d2_u10(values)), map(std::expm1));
-  }
-  Vectorized<double> fmod(const Vectorized<double>& q) const {USE_SLEEF(
-      { return Vectorized<double>(Sleef_fmodd2(values, q)); },
-      {
-        __at_align__ double tmp[size()];
-        __at_align__ double tmp_q[size()];
-        store(tmp);
-        q.store(tmp_q);
-        for (int64_t i = 0; i < size(); i++) {
-          tmp[i] = std::fmod(tmp[i], tmp_q[i]);
-        }
-        return loadu(tmp);
-      })} Vectorized<double> hypot(const Vectorized<double>& b) const {
-      USE_SLEEF(
-          { return Vectorized<double>(Sleef_hypotd2_u05(values, b)); },
-          {
-            __at_align__ double tmp[size()];
-            __at_align__ double tmp_b[size()];
-            store(tmp);
-            b.store(tmp_b);
-            for (int64_t i = 0; i < size(); i++) {
-              tmp[i] = std::hypot(tmp[i], tmp_b[i]);
-            }
-            return loadu(tmp);
-          })} Vectorized<double> i0() const {
-    return map(calc_i0);
-  }
-  Vectorized<double> nextafter(const Vectorized<double>& b) const {USE_SLEEF(
-      { return Vectorized<double>(Sleef_nextafterd2(values, b)); },
-      {
-        __at_align__ double tmp[size()];
-        __at_align__ double tmp_b[size()];
-        store(tmp);
-        b.store(tmp_b);
-        for (int64_t i = 0; i < size(); ++i) {
-          tmp[i] = std::nextafter(tmp[i], tmp_b[i]);
-        }
-        return loadu(tmp);
-      })} Vectorized<double> log() const {
-    return USE_SLEEF(
-        Vectorized<double>(Sleef_logd2_u10(values)), map(std::log));
-  }
-  Vectorized<double> log2() const {
-    return USE_SLEEF(
-        Vectorized<double>(Sleef_log2d2_u10(values)), map(std::log2));
-  }
-  Vectorized<double> log10() const {
-    return USE_SLEEF(
-        Vectorized<double>(Sleef_log10d2_u10(values)), map(std::log10));
-  }
-  Vectorized<double> log1p() const {
-    return USE_SLEEF(
-        Vectorized<double>(Sleef_log1pd2_u10(values)), map(std::log1p));
-  }
-  Vectorized<double> frac() const;
-  Vectorized<double> sin() const {
-    return USE_SLEEF(
-        Vectorized<double>(Sleef_sind2_u10(values)), map(std::sin));
-  }
-  Vectorized<double> sinh() const {
-    return USE_SLEEF(
-        Vectorized<double>(Sleef_sinhd2_u10(values)), map(std::sinh));
-  }
-  Vectorized<double> cos() const {
-    return USE_SLEEF(
-        Vectorized<double>(Sleef_cosd2_u10(values)), map(std::cos));
-  }
-  Vectorized<double> cosh() const {
-    return USE_SLEEF(
-        Vectorized<double>(Sleef_coshd2_u10(values)), map(std::cosh));
-  }
-  Vectorized<double> pow(const Vectorized<double>& b) const {USE_SLEEF(
-      { return Vectorized<double>(Sleef_powd2_u10(values, b)); },
-      {
-        __at_align__ double tmp[size()];
-        __at_align__ double tmp_b[size()];
-        store(tmp);
-        b.store(tmp_b);
-        for (int64_t i = 0; i < size(); i++) {
-          tmp[i] = std::pow(tmp[i], tmp_b[i]);
-        }
-        return loadu(tmp);
-      })} // Comparison using the _CMP_**_OQ predicate.
-          //   `O`: get false if an operand is NaN
-          //   `Q`: do not raise if an operand is NaN
-  Vectorized<double> tan() const {
-    return USE_SLEEF(
-        Vectorized<double>(Sleef_tand2_u10(values)), map(std::tan));
-  }
-  Vectorized<double> tanh() const {
-    return USE_SLEEF(
-        Vectorized<double>(Sleef_tanhd2_u10(values)), map(std::tanh));
-  }
-  Vectorized<double> lgamma() const {
-    return USE_SLEEF(
-        Vectorized<double>(Sleef_lgammad2_u10(values)), map(std::lgamma));
-  }
-  Vectorized<double> erfinv() const {
-    return map(calc_erfinv);
-  }
-  Vectorized<double> exp_u20() const {
-    return exp();
-  }
-  Vectorized<double> fexp_u20() const {
-    return exp();
-  }
-  Vectorized<double> i0e() const {
-    return map(calc_i0e);
-  }
-  Vectorized<double> digamma() const {
-    return map(calc_digamma);
-  }
-  Vectorized<double> igamma(const Vectorized<double>& x) const {
-    __at_align__ double tmp[size()];
-    __at_align__ double tmp_x[size()];
-    store(tmp);
-    x.store(tmp_x);
-    for (int64_t i = 0; i < size(); i++) {
-      tmp[i] = calc_igamma(tmp[i], tmp_x[i]);
-    }
-    return loadu(tmp);
-  }
-  Vectorized<double> igammac(const Vectorized<double>& x) const {
-    __at_align__ double tmp[size()];
-    __at_align__ double tmp_x[size()];
-    store(tmp);
-    x.store(tmp_x);
-    for (int64_t i = 0; i < size(); i++) {
-      tmp[i] = calc_igammac(tmp[i], tmp_x[i]);
-    }
-    return loadu(tmp);
-  }
-  Vectorized<double> ceil() const {
-    return vrndpq_f64(values);
-  }
-  Vectorized<double> floor() const {
-    return vrndmq_f64(values);
-  }
-  Vectorized<double> neg() const {
-    return vnegq_f64(values);
-  }
-  Vectorized<double> round() const {
-    return vrndiq_f64(values);
-  }
-  Vectorized<double> trunc() const {
-    return vrndq_f64(values);
-  }
-  Vectorized<double> sqrt() const {
-    return vsqrtq_f64(values);
-  }
-  Vectorized<double> reciprocal() const {
-    return vdivq_f64(vdupq_n_f64(1.0), values);
-  }
-  Vectorized<double> rsqrt() const {
-    return vdivq_f64(vdupq_n_f64(1.0), vsqrtq_f64(values));
-  }
-  double reduce_add() const {
-    return vaddvq_f64(values);
-  }
-  double reduce_max() const {
-    return vmaxvq_f64(values);
-  }
-  Vectorized<double> operator==(const Vectorized<double>& other) const {
-    return Vectorized<double>(
-        vreinterpretq_f64_u64(vceqq_f64(values, other.values)));
-  }
-
-  Vectorized<double> operator!=(const Vectorized<double>& other) const {
-    float64x2_t r0 = vreinterpretq_f64_u32(
-        vmvnq_u32(vreinterpretq_u32_u64(vceqq_f64(values, other.values))));
-    return Vectorized<double>(r0);
-  }
-
-  Vectorized<double> operator<(const Vectorized<double>& other) const {
-    return Vectorized<double>(
-        vreinterpretq_f64_u64(vcltq_f64(values, other.values)));
-  }
-
-  Vectorized<double> operator<=(const Vectorized<double>& other) const {
-    return Vectorized<double>(
-        vreinterpretq_f64_u64(vcleq_f64(values, other.values)));
-  }
-
-  Vectorized<double> operator>(const Vectorized<double>& other) const {
-    return Vectorized<double>(
-        vreinterpretq_f64_u64(vcgtq_f64(values, other.values)));
-  }
-
-  Vectorized<double> operator>=(const Vectorized<double>& other) const {
-    return Vectorized<double>(
-        vreinterpretq_f64_u64(vcgeq_f64(values, other.values)));
-  }
-
-  Vectorized<double> eq(const Vectorized<double>& other) const;
-  Vectorized<double> ne(const Vectorized<double>& other) const;
-  Vectorized<double> gt(const Vectorized<double>& other) const;
-  Vectorized<double> ge(const Vectorized<double>& other) const;
-  Vectorized<double> lt(const Vectorized<double>& other) const;
-  Vectorized<double> le(const Vectorized<double>& other) const;
-};
-
-template <>
-Vectorized<double> inline operator+(
-    const Vectorized<double>& a,
-    const Vectorized<double>& b) {
-  return vaddq_f64(a, b);
-}
-
-template <>
-Vectorized<double> inline operator-(
-    const Vectorized<double>& a,
-    const Vectorized<double>& b) {
-  return vsubq_f64(a, b);
-}
-
-template <>
-Vectorized<double> inline operator*(
-    const Vectorized<double>& a,
-    const Vectorized<double>& b) {
-  return vmulq_f64(a, b);
-}
-
-template <>
-Vectorized<double> inline operator/(
-    const Vectorized<double>& a,
-    const Vectorized<double>& b) {
-  return vdivq_f64(a, b);
-}
-
-// frac. Implement this here so we can use subtraction
-Vectorized<double> inline Vectorized<double>::frac() const {
-  return *this - this->trunc();
-}
-
-// Implements the IEEE 754 201X `maximum` operation, which propagates NaN if
-// either input is a NaN.
-template <>
-Vectorized<double> inline maximum(
-    const Vectorized<double>& a,
-    const Vectorized<double>& b) {
-  return vmaxq_f64(a, b);
-}
-
-// Implements the IEEE 754 201X `minimum` operation, which propagates NaN if
-// either input is a NaN.
-template <>
-Vectorized<double> inline minimum(
-    const Vectorized<double>& a,
-    const Vectorized<double>& b) {
-  return vminq_f64(a, b);
-}
-
-template <>
-Vectorized<double> inline clamp(
-    const Vectorized<double>& a,
-    const Vectorized<double>& min,
-    const Vectorized<double>& max) {
-  return vminq_f64(max, vmaxq_f64(min, a));
-}
-
-template <>
-Vectorized<double> inline clamp_max(
-    const Vectorized<double>& a,
-    const Vectorized<double>& max) {
-  return vminq_f64(max, a);
-}
-
-template <>
-Vectorized<double> inline clamp_min(
-    const Vectorized<double>& a,
-    const Vectorized<double>& min) {
-  return vmaxq_f64(min, a);
-}
-
-template <>
-Vectorized<double> inline operator&(
-    const Vectorized<double>& a,
-    const Vectorized<double>& b) {
-  return vreinterpretq_f64_u64(
-      vandq_u64(vreinterpretq_u64_f64(a), vreinterpretq_u64_f64(b)));
-}
-
-template <>
-Vectorized<double> inline operator|(
-    const Vectorized<double>& a,
-    const Vectorized<double>& b) {
-  return vreinterpretq_f64_u64(
-      vorrq_u64(vreinterpretq_u64_f64(a), vreinterpretq_u64_f64(b)));
-}
-
-template <>
-Vectorized<double> inline operator^(
-    const Vectorized<double>& a,
-    const Vectorized<double>& b) {
-  return vreinterpretq_f64_u64(
-      veorq_u64(vreinterpretq_u64_f64(a), vreinterpretq_u64_f64(b)));
-}
-
-inline Vectorized<double> Vectorized<double>::eq(
-    const Vectorized<double>& other) const {
-  return (*this == other) & Vectorized<double>(1.0);
-}
-
-inline Vectorized<double> Vectorized<double>::ne(
-    const Vectorized<double>& other) const {
-  return (*this != other) & Vectorized<double>(1.0);
-}
-
-inline Vectorized<double> Vectorized<double>::gt(
-    const Vectorized<double>& other) const {
-  return (*this > other) & Vectorized<double>(1.0);
-}
-
-inline Vectorized<double> Vectorized<double>::ge(
-    const Vectorized<double>& other) const {
-  return (*this >= other) & Vectorized<double>(1.0);
-}
-
-inline Vectorized<double> Vectorized<double>::lt(
-    const Vectorized<double>& other) const {
-  return (*this < other) & Vectorized<double>(1.0);
-}
-
-inline Vectorized<double> Vectorized<double>::le(
-    const Vectorized<double>& other) const {
-  return (*this <= other) & Vectorized<double>(1.0);
-}
-
-template <>
-Vectorized<double> inline fmadd(
-    const Vectorized<double>& a,
-    const Vectorized<double>& b,
-    const Vectorized<double>& c) {
-  return vfmaq_f64(c, a, b);
-}
-
-template <>
-Vectorized<double> inline fnmadd(
-    const Vectorized<double>& a,
-    const Vectorized<double>& b,
-    const Vectorized<double>& c) {
-  return vfmsq_f64(c, a, b);
-}
-
-template <>
-Vectorized<double> inline fmsub(
-    const Vectorized<double>& a,
-    const Vectorized<double>& b,
-    const Vectorized<double>& c) {
-  return vfmaq_f64(vnegq_f64(c), a, b);
-}
-
-template <>
-Vectorized<double> inline fnmsub(
-    const Vectorized<double>& a,
-    const Vectorized<double>& b,
-    const Vectorized<double>& c) {
-  return vfmsq_f64(vnegq_f64(c), a, b);
-}
-
-} // namespace CPU_CAPABILITY
-} // namespace at::vec

aten/src/ATen/cpu/vec/vec128/vec128_uint_aarch64.h

@@ -1,378 +0,0 @@
-#pragma once
-
-#include <ATen/cpu/vec/intrinsics.h>
-#include <ATen/cpu/vec/vec_base.h>
-#include <c10/macros/Macros.h>
-#include <c10/util/irange.h>
-
-namespace at::vec {
-// Note [CPU_CAPABILITY namespace]
-// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-// This header, and all of its subheaders, will be compiled with
-// different architecture flags for each supported set of vector
-// intrinsics. So we need to make sure they aren't inadvertently
-// linked together. We do this by declaring objects in an `inline
-// namespace` which changes the name mangling, but can still be
-// accessed as `at::vec`.
-inline namespace CPU_CAPABILITY {
-
-#define VEC_UINT_NEON_TEMPLATE(vl, bit)                                       \
-  template <>                                                                 \
-  struct is_vec_specialized_for<uint##bit##_t> : std::bool_constant<true> {}; \
-                                                                              \
-  template <>                                                                 \
-  class Vectorized<uint##bit##_t> {                                           \
-    using neon_type = uint##bit##x##vl##_t;                                   \
-                                                                              \
-   private:                                                                   \
-    neon_type values;                                                         \
-                                                                              \
-   public:                                                                    \
-    using value_type = uint##bit##_t;                                         \
-    using size_type = int;                                                    \
-    static constexpr size_type size() {                                       \
-      return vl;                                                              \
-    }                                                                         \
-    Vectorized() {                                                            \
-      values = vdupq_n_u##bit(0);                                             \
-    }                                                                         \
-    Vectorized(neon_type v) : values(v) {}                                    \
-    Vectorized(uint##bit##_t val);                                            \
-    template <                                                                \
-        typename... Args,                                                     \
-        typename = std::enable_if_t<(sizeof...(Args) == size())>>             \
-    Vectorized(Args... vals) {                                                \
-      __at_align__ uint##bit##_t buffer[size()] = {vals...};                  \
-      values = vld1q_u##bit(buffer);                                          \
-    }                                                                         \
-    operator neon_type() const {                                              \
-      return values;                                                          \
-    }                                                                         \
-    static Vectorized<uint##bit##_t> loadu(                                   \
-        const void* ptr,                                                      \
-        uint64_t count = size());                                             \
-    void store(void* ptr, uint64_t count = size()) const;                     \
-    template <uint64_t mask>                                                  \
-    static Vectorized<uint##bit##_t> blend(                                   \
-        const Vectorized<uint##bit##_t>& a,                                   \
-        const Vectorized<uint##bit##_t>& b);                                  \
-    static Vectorized<uint##bit##_t> blendv(                                  \
-        const Vectorized<uint##bit##_t>& a,                                   \
-        const Vectorized<uint##bit##_t>& b,                                   \
-        const Vectorized<uint##bit##_t>& mask_) {                             \
-      return vbslq_u##bit(mask_.values, b, a);                                \
-    }                                                                         \
-    template <typename step_t>                                                \
-    static Vectorized<uint##bit##_t> arange(                                  \
-        value_type base = 0,                                                  \
-        step_t step = static_cast<step_t>(1));                                \
-    static Vectorized<uint##bit##_t> set(                                     \
-        const Vectorized<uint##bit##_t>& a,                                   \
-        const Vectorized<uint##bit##_t>& b,                                   \
-        uint64_t count = size());                                             \
-    const uint##bit##_t& operator[](uint idx) const = delete;                 \
-    uint##bit##_t& operator[](uint idx) = delete;                             \
-    Vectorized<uint##bit##_t> abs() const {                                   \
-      return values;                                                          \
-    }                                                                         \
-    Vectorized<uint##bit##_t> real() const {                                  \
-      return values;                                                          \
-    }                                                                         \
-    Vectorized<uint##bit##_t> imag() const {                                  \
-      return vdupq_n_u##bit(0);                                               \
-    }                                                                         \
-    Vectorized<uint##bit##_t> conj() const {                                  \
-      return values;                                                          \
-    }                                                                         \
-    Vectorized<uint##bit##_t> neg() const {                                   \
-      return vreinterpretq_u##bit##_s##bit(                                   \
-          vnegq_s##bit(vreinterpretq_s##bit##_u##bit(values)));               \
-    }                                                                         \
-    uint##bit##_t reduce_add() const {                                        \
-      return vaddvq_u##bit(values);                                           \
-    }                                                                         \
-    uint##bit##_t reduce_max() const;                                         \
-    Vectorized<uint##bit##_t> operator==(                                     \
-        const Vectorized<uint##bit##_t>& other) const {                       \
-      return Vectorized<value_type>(vceqq_u##bit(values, other.values));      \
-    }                                                                         \
-    Vectorized<uint##bit##_t> operator!=(                                     \
-        const Vectorized<uint##bit##_t>& other) const;                        \
-    Vectorized<uint##bit##_t> operator<(                                      \
-        const Vectorized<uint##bit##_t>& other) const {                       \
-      return Vectorized<value_type>(vcltq_u##bit(values, other.values));      \
-    }                                                                         \
-    Vectorized<uint##bit##_t> operator<=(                                     \
-        const Vectorized<uint##bit##_t>& other) const {                       \
-      return Vectorized<value_type>(vcleq_u##bit(values, other.values));      \
-    }                                                                         \
-    Vectorized<uint##bit##_t> operator>(                                      \
-        const Vectorized<uint##bit##_t>& other) const {                       \
-      return Vectorized<value_type>(vcgtq_u##bit(values, other.values));      \
-    }                                                                         \
-    Vectorized<uint##bit##_t> operator>=(                                     \
-        const Vectorized<uint##bit##_t>& other) const {                       \
-      return Vectorized<value_type>(vcgeq_u##bit(values, other.values));      \
-    }                                                                         \
-    Vectorized<uint##bit##_t> eq(                                             \
-        const Vectorized<uint##bit##_t>& other) const;                        \
-    Vectorized<uint##bit##_t> ne(                                             \
-        const Vectorized<uint##bit##_t>& other) const;                        \
-    Vectorized<uint##bit##_t> gt(                                             \
-        const Vectorized<uint##bit##_t>& other) const;                        \
-    Vectorized<uint##bit##_t> ge(                                             \
-        const Vectorized<uint##bit##_t>& other) const;                        \
-    Vectorized<uint##bit##_t> lt(                                             \
-        const Vectorized<uint##bit##_t>& other) const;                        \
-    Vectorized<uint##bit##_t> le(                                             \
-        const Vectorized<uint##bit##_t>& other) const;                        \
-  };                                                                          \
-  template <>                                                                 \
-  Vectorized<uint##bit##_t> inline operator+(                                 \
-      const Vectorized<uint##bit##_t>& a,                                     \
-      const Vectorized<uint##bit##_t>& b) {                                   \
-    return vaddq_u##bit(a, b);                                                \
-  }                                                                           \
-  template <>                                                                 \
-  Vectorized<uint##bit##_t> inline operator-(                                 \
-      const Vectorized<uint##bit##_t>& a,                                     \
-      const Vectorized<uint##bit##_t>& b) {                                   \
-    return vsubq_u##bit(a, b);                                                \
-  }                                                                           \
-  template <>                                                                 \
-  Vectorized<uint##bit##_t> inline operator&(                                 \
-      const Vectorized<uint##bit##_t>& a,                                     \
-      const Vectorized<uint##bit##_t>& b) {                                   \
-    return vandq_u##bit(a, b);                                                \
-  }                                                                           \
-  template <>                                                                 \
-  Vectorized<uint##bit##_t> inline operator|(                                 \
-      const Vectorized<uint##bit##_t>& a,                                     \
-      const Vectorized<uint##bit##_t>& b) {                                   \
-    return vorrq_u##bit(a, b);                                                \
-  }                                                                           \
-  template <>                                                                 \
-  Vectorized<uint##bit##_t> inline operator^(                                 \
-      const Vectorized<uint##bit##_t>& a,                                     \
-      const Vectorized<uint##bit##_t>& b) {                                   \
-    return veorq_u##bit(a, b);                                                \
-  }                                                                           \
-  Vectorized<uint##bit##_t> inline Vectorized<uint##bit##_t>::eq(             \
-      const Vectorized<uint##bit##_t>& other) const {                         \
-    return (*this == other) & Vectorized<uint##bit##_t>(1);                   \
-  }                                                                           \
-  Vectorized<uint##bit##_t> inline Vectorized<uint##bit##_t>::ne(             \
-      const Vectorized<uint##bit##_t>& other) const {                         \
-    return (*this != other) & Vectorized<uint##bit##_t>(1);                   \
-  }                                                                           \
-  Vectorized<uint##bit##_t> inline Vectorized<uint##bit##_t>::gt(             \
-      const Vectorized<uint##bit##_t>& other) const {                         \
-    return (*this > other) & Vectorized<uint##bit##_t>(1);                    \
-  }                                                                           \
-  Vectorized<uint##bit##_t> inline Vectorized<uint##bit##_t>::ge(             \
-      const Vectorized<uint##bit##_t>& other) const {                         \
-    return (*this >= other) & Vectorized<uint##bit##_t>(1);                   \
-  }                                                                           \
-  Vectorized<uint##bit##_t> inline Vectorized<uint##bit##_t>::lt(             \
-      const Vectorized<uint##bit##_t>& other) const {                         \
-    return (*this < other) & Vectorized<uint##bit##_t>(1);                    \
-  }                                                                           \
-  Vectorized<uint##bit##_t> inline Vectorized<uint##bit##_t>::le(             \
-      const Vectorized<uint##bit##_t>& other) const {                         \
-    return (*this <= other) & Vectorized<uint##bit##_t>(1);                   \
-  }
-
-VEC_UINT_NEON_TEMPLATE(16, 8)
-
-inline uint8_t Vectorized<uint8_t>::reduce_max() const {
-  return vmaxvq_u8(values);
-}
-
-template <>
-Vectorized<uint8_t> inline operator*(
-    const Vectorized<uint8_t>& a,
-    const Vectorized<uint8_t>& b) {
-  return vmulq_u8(a, b);
-}
-
-template <>
-inline Vectorized<uint8_t> operator~(const Vectorized<uint8_t>& a) {
-  return vmvnq_u8(a);
-}
-
-inline Vectorized<uint8_t> Vectorized<uint8_t>::operator!=(
-    const Vectorized<uint8_t>& other) const {
-  return ~(*this == other);
-}
-
-template <>
-Vectorized<uint8_t> inline minimum(
-    const Vectorized<uint8_t>& a,
-    const Vectorized<uint8_t>& b) {
-  return vminq_u8(a, b);
-}
-
-template <>
-Vectorized<uint8_t> inline maximum(
-    const Vectorized<uint8_t>& a,
-    const Vectorized<uint8_t>& b) {
-  return vmaxq_u8(a, b);
-}
-
-template <uint64_t mask>
-Vectorized<uint8_t> Vectorized<uint8_t>::blend(
-    const Vectorized<uint8_t>& a,
-    const Vectorized<uint8_t>& b) {
-  // Build an array of flags: each bit of element is 1 if the corresponding bit
-  // in 'mask' is set, 0 otherwise.
-  uint8x16_t maskArray = {
-      (mask & 1LL) ? 0xFF : 0,
-      (mask & 2LL) ? 0xFF : 0,
-      (mask & 4LL) ? 0xFF : 0,
-      (mask & 8LL) ? 0xFF : 0,
-      (mask & 16LL) ? 0xFF : 0,
-      (mask & 32LL) ? 0xFF : 0,
-      (mask & 64LL) ? 0xFF : 0,
-      (mask & 128LL) ? 0xFF : 0,
-      (mask & 256LL) ? 0xFF : 0,
-      (mask & 512LL) ? 0xFF : 0,
-      (mask & 1024LL) ? 0xFF : 0,
-      (mask & 2048LL) ? 0xFF : 0,
-      (mask & 4096LL) ? 0xFF : 0,
-      (mask & 8192LL) ? 0xFF : 0,
-      (mask & 16384LL) ? 0xFF : 0,
-      (mask & 32768LL) ? 0xFF : 0};
-  // Use BSL to select elements from b where the mask is 1, else from a
-  return vbslq_u8(maskArray, b.values, a.values);
-}
-
-#define VEC_UINT_NEON_OPS(vl, bit)                                             \
-  inline Vectorized<uint##bit##_t>::Vectorized(uint##bit##_t val) {            \
-    values = vdupq_n_u##bit(val);                                              \
-  }                                                                            \
-  inline Vectorized<uint##bit##_t> Vectorized<uint##bit##_t>::loadu(           \
-      const void* ptr, uint64_t count) {                                       \
-    if (count == size()) {                                                     \
-      return vld1q_u##bit(reinterpret_cast<const uint##bit##_t*>(ptr));        \
-    } else {                                                                   \
-      __at_align__ uint##bit##_t tmp_values[size()];                           \
-      for (const auto i : c10::irange(size())) {                               \
-        tmp_values[i] = 0;                                                     \
-      }                                                                        \
-      std::memcpy(                                                             \
-          tmp_values,                                                          \
-          reinterpret_cast<const uint##bit##_t*>(ptr),                         \
-          count * sizeof(uint##bit##_t));                                      \
-      return vld1q_u##bit(reinterpret_cast<const uint##bit##_t*>(tmp_values)); \
-    }                                                                          \
-  }                                                                            \
-  inline void Vectorized<uint##bit##_t>::store(void* ptr, uint64_t count)      \
-      const {                                                                  \
-    if (count == size()) {                                                     \
-      vst1q_u##bit(reinterpret_cast<uint##bit##_t*>(ptr), values);             \
-    } else {                                                                   \
-      uint##bit##_t tmp_values[size()];                                        \
-      vst1q_u##bit(reinterpret_cast<uint##bit##_t*>(tmp_values), values);      \
-      std::memcpy(ptr, tmp_values, count * sizeof(uint##bit##_t));             \
-    }                                                                          \
-  }
-
-VEC_UINT_NEON_OPS(16, 8)
-
-template <typename step_t>
-inline Vectorized<uint8_t> Vectorized<uint8_t>::arange(
-    uint8_t base,
-    step_t step) {
-  const Vectorized<uint8_t> base_vec(base);
-  const Vectorized<uint8_t> step_vec(step);
-  const uint8x16_t step_sizes = {
-      0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15};
-  return vmlaq_u8(base_vec, step_sizes, step_vec);
-}
-
-template <>
-Vectorized<uint8_t> inline operator>>(
-    const Vectorized<uint8_t>& a,
-    const Vectorized<uint8_t>& b) {
-  uint8x16_t x = a;
-  uint8x16_t bound = vdupq_n_u8(8);
-  uint8x16_t z = vminq_u8(b, bound);
-  return x >> z;
-}
-
-template <>
-Vectorized<uint8_t> inline operator<<(
-    const Vectorized<uint8_t>& a,
-    const Vectorized<uint8_t>& b) {
-  uint8x16_t bound = vdupq_n_u8(8);
-  uint8x16_t z = vminq_u8(b, bound);
-  return vshlq_u8(a, vreinterpretq_s8_u8(z));
-}
-
-inline Vectorized<uint8_t> Vectorized<uint8_t>::set(
-    const Vectorized<uint8_t>& a,
-    const Vectorized<uint8_t>& b,
-    uint64_t count) {
-  if (count == 0) {
-    return a;
-  } else if (count >= 16) {
-    return b;
-  } else {
-    // Build an array of flags: each bit of element is 1 if the corresponding
-    // bit in 'mask' is set, 0 otherwise.
-    uint8x16_t maskArray = {
-        static_cast<uint8_t>((count >= 1LL) ? 0xFF : 0),
-        static_cast<uint8_t>((count >= 2LL) ? 0xFF : 0),
-        static_cast<uint8_t>((count >= 3LL) ? 0xFF : 0),
-        static_cast<uint8_t>((count >= 4LL) ? 0xFF : 0),
-        static_cast<uint8_t>((count >= 5LL) ? 0xFF : 0),
-        static_cast<uint8_t>((count >= 6LL) ? 0xFF : 0),
-        static_cast<uint8_t>((count >= 7LL) ? 0xFF : 0),
-        static_cast<uint8_t>((count >= 8LL) ? 0xFF : 0),
-        static_cast<uint8_t>((count >= 9LL) ? 0xFF : 0),
-        static_cast<uint8_t>((count >= 10LL) ? 0xFF : 0),
-        static_cast<uint8_t>((count >= 11LL) ? 0xFF : 0),
-        static_cast<uint8_t>((count >= 12LL) ? 0xFF : 0),
-        static_cast<uint8_t>((count >= 13LL) ? 0xFF : 0),
-        static_cast<uint8_t>((count >= 14LL) ? 0xFF : 0),
-        static_cast<uint8_t>((count >= 15LL) ? 0xFF : 0),
-        0};
-
-    // Use BSL to select elements from b where the mask is 1, else from a
-    return vbslq_u8(maskArray, b.values, a.values);
-  }
-}
-
-template <>
-Vectorized<uint8_t> inline operator/(
-    const Vectorized<uint8_t>& a,
-    const Vectorized<uint8_t>& b) {
-  uint8x16_t x = a;
-  uint8x16_t y = b;
-  return x / y;
-}
-
-template <>
-Vectorized<uint8_t> inline clamp(
-    const Vectorized<uint8_t>& a,
-    const Vectorized<uint8_t>& min,
-    const Vectorized<uint8_t>& max) {
-  return minimum(max, maximum(min, a));
-}
-
-template <>
-Vectorized<uint8_t> inline clamp_max(
-    const Vectorized<uint8_t>& a,
-    const Vectorized<uint8_t>& max) {
-  return minimum(max, a);
-}
-
-template <>
-Vectorized<uint8_t> inline clamp_min(
-    const Vectorized<uint8_t>& a,
-    const Vectorized<uint8_t>& min) {
-  return maximum(min, a);
-}
-
-} // namespace CPU_CAPABILITY
-} // namespace at::vec

aten/src/ATen/cpu/vec/vec256/vec256_qint.h

@@ -1390,7 +1390,7 @@ std::pair<Vectorized<float>, Vectorized<float>> inline convert_int8_to_float(
 
 std::pair<Vectorized<float>, Vectorized<float>> inline convert_int8_to_float(
     at::vec::Vectorized<uint8_t> src) {
-  auto u8x8 = vget_low_u8(src);
+  auto u8x8 = vld1_u8(src.operator const uint8_t*());
   auto u16x8 = vmovl_u8(u8x8);
   auto u32x4_hi = vmovl_u16(vget_high_u16(u16x8));
   auto u32x4_lo = vmovl_u16(vget_low_u16(u16x8));
@@ -1412,7 +1412,7 @@ Vectorized<float> inline convert_int8_half_register_to_float(
 
 Vectorized<float> inline convert_int8_half_register_to_float(
     at::vec::Vectorized<uint8_t> src) {
-  auto u8x8 = vget_low_u8(src);
+  auto u8x8 = vld1_u8(src.operator const uint8_t*());
   auto u16x8 = vmovl_u8(u8x8);
   auto u32x4_lo = vmovl_u16(vget_low_u16(u16x8));
 

aten/src/ATen/cuda/CUDAGreenContext.cpp

@@ -1,192 +0,0 @@
-#include <ATen/cuda/CUDAGreenContext.h>
-
-namespace at::cuda {
-  GreenContext::GreenContext(uint32_t device_id, uint32_t num_sms) {
-#if CUDA_HAS_GREEN_CONTEXT
-    int driver_version;
-    C10_CUDA_CHECK(cudaDriverGetVersion(&driver_version));
-    TORCH_CHECK(
-        driver_version >= 12080, "cuda driver too old to use green context!");
-    CUcontext pctx = nullptr;
-    C10_CUDA_DRIVER_CHECK(c10::cuda::DriverAPI::get()->cuCtxGetCurrent_(&pctx));
-    if (C10_UNLIKELY(!pctx)) {
-      TORCH_WARN(
-          "Attempted to create a green context but"
-          " there was no primary context! Creating a primary context...");
-
-      cudaFree(0);
-    }
-
-    CUdevice device;
-    device_id_ = device_id;
-    C10_CUDA_DRIVER_CHECK(
-        c10::cuda::DriverAPI::get()->cuDeviceGet_(&device, device_id));
-
-    // Get device resources
-    CUdevResource device_resource;
-    C10_CUDA_DRIVER_CHECK(c10::cuda::DriverAPI::get()->cuDeviceGetDevResource_(
-        device, &device_resource, CU_DEV_RESOURCE_TYPE_SM));
-
-    // Split resources
-    std::vector<CUdevResource> result(1);
-    auto result_data = result.data();
-    unsigned int nb_groups = 1;
-    CUdevResource remaining;
-
-    C10_CUDA_DRIVER_CHECK(
-        c10::cuda::DriverAPI::get()->cuDevSmResourceSplitByCount_(
-            result_data,
-            &nb_groups,
-            &device_resource,
-            &remaining,
-            0, // default flags
-            num_sms));
-
-    TORCH_CHECK(nb_groups == 1, "Failed to create single resource group");
-
-    // Generate resource descriptor
-    CUdevResourceDesc desc;
-    C10_CUDA_DRIVER_CHECK(
-        c10::cuda::DriverAPI::get()->cuDevResourceGenerateDesc_(
-            &desc, result_data, 1));
-
-    // Create green context
-    // CU_GREEN_CTX_DEFAULT_STREAM is required per docs:
-    // https://docs.nvidia.com/cuda/cuda-driver-api/group__CUDA__GREEN__CONTEXTS.html
-    C10_CUDA_DRIVER_CHECK(c10::cuda::DriverAPI::get()->cuGreenCtxCreate_(
-        &green_ctx_, desc, device, CU_GREEN_CTX_DEFAULT_STREAM));
-
-    // Convert to regular context
-    C10_CUDA_DRIVER_CHECK(
-        c10::cuda::DriverAPI::get()->cuCtxFromGreenCtx_(&context_, green_ctx_));
-    TORCH_CHECK(context_, "Green ctx conversion to regular ctx failed!");
-#else
-    TORCH_CHECK(false, "Green Context is only supported on CUDA 12.8+!");
-#endif
-  }
-
-  std::unique_ptr<GreenContext> GreenContext::create(
-      uint32_t num_sms,
-      std::optional<uint32_t> device_id) {
-#if CUDA_HAS_GREEN_CONTEXT
-    if (!device_id.has_value()) {
-      device_id = at::cuda::current_device();
-    }
-    return std::make_unique<GreenContext>(device_id.value(), num_sms);
-#else
-    TORCH_CHECK(false, "Green Context is only supported on CUDA 12.8+!");
-#endif
-  }
-
-  // Implement move operations
-  GreenContext::GreenContext(GreenContext&& other) noexcept{
-#if CUDA_HAS_GREEN_CONTEXT
-    device_id_ = std::exchange(other.device_id_, -1);
-    green_ctx_ = std::exchange(other.green_ctx_, nullptr);
-    context_ = std::exchange(other.context_, nullptr);
-    parent_stream_ = std::exchange(other.parent_stream_, nullptr);
-#else
-    TORCH_CHECK(false, "Green Context is only supported on CUDA 12.8+!");
-#endif
-  }
-
-  GreenContext& GreenContext::operator=(GreenContext&& other) noexcept{
-#if CUDA_HAS_GREEN_CONTEXT
-    if (this != &other) {
-      // Clean up current resources
-      if (green_ctx_) {
-        CUcontext current = nullptr;
-        C10_CUDA_DRIVER_CHECK(
-            c10::cuda::DriverAPI::get()->cuCtxGetCurrent_(&current));
-        if (current == context_) {
-          TORCH_CHECK(
-              false,
-              "attempting to overwrite current green ctx "
-              "when it is active!");
-        }
-        C10_CUDA_DRIVER_CHECK(c10::cuda::DriverAPI::get()->cuGreenCtxDestroy_(green_ctx_));
-      }
-
-      // Take ownership of other's resources
-      device_id_ = std::exchange(other.device_id_, -1);
-      green_ctx_ = std::exchange(other.green_ctx_, nullptr);
-      context_ = std::exchange(other.context_, nullptr);
-      parent_stream_ = std::exchange(other.parent_stream_, nullptr);
-    }
-    return *this;
-#else
-    TORCH_CHECK(false, "Green Context is only supported on CUDA 12.8+!");
-#endif
-  }
-
-  GreenContext::~GreenContext() noexcept{
-#if CUDA_HAS_GREEN_CONTEXT
-    C10_CUDA_DRIVER_CHECK(
-        c10::cuda::DriverAPI::get()->cuGreenCtxDestroy_(green_ctx_));
-#else
-    TORCH_CHECK(false, "Green Context is only supported on CUDA 12.8+!");
-#endif
-  }
-
-  // Get the underlying CUDA context
-  CUcontext GreenContext::getContext() const {
-#if CUDA_HAS_GREEN_CONTEXT
-    return context_;
-#else
-    TORCH_CHECK(false, "Green Context is only supported on CUDA 12.8+!");
-#endif
-  }
-
-  // Get the underlying green context
-#if CUDA_HAS_GREEN_CONTEXT
-  CUgreenCtx GreenContext::getGreenContext() const {
-    return green_ctx_;
-  }
-#endif
-
-  // Make this context current
-  void GreenContext::setContext() {
-#if CUDA_HAS_GREEN_CONTEXT
-    auto current_stream = c10::cuda::getCurrentCUDAStream();
-    parent_stream_ = current_stream.stream();
-
-    at::cuda::CUDAEvent ev;
-    ev.record(current_stream);
-
-    CUcontext current = nullptr;
-    C10_CUDA_DRIVER_CHECK(
-        c10::cuda::DriverAPI::get()->cuCtxGetCurrent_(&current));
-    if (!current) {
-      C10_CUDA_DRIVER_CHECK(
-          c10::cuda::DriverAPI::get()->cuCtxSetCurrent_(context_));
-    } else {
-      C10_CUDA_DRIVER_CHECK(
-          c10::cuda::DriverAPI::get()->cuCtxPushCurrent_(context_));
-    }
-    // currently hardcodes the new green context to use the default stream
-    // TODO(eqy): consider creating a new stream if e.g., it allows interop
-    // with CUDA Graph captures etc.
-    auto default_stream = c10::cuda::getDefaultCUDAStream();
-    ev.block(default_stream);
-    c10::cuda::setCurrentCUDAStream(default_stream);
-#else
-    TORCH_CHECK(false, "Green Context is only supported on CUDA 12.8+!");
-#endif
-  }
-
-  void GreenContext::popContext() {
-#if CUDA_HAS_GREEN_CONTEXT
-    // see above note about stream being hardcoded to the default stream
-    at::cuda::CUDAEvent ev;
-    ev.record(c10::cuda::getCurrentCUDAStream());
-    CUcontext popped;
-    C10_CUDA_DRIVER_CHECK(
-        c10::cuda::DriverAPI::get()->cuCtxPopCurrent_(&popped));
-    TORCH_INTERNAL_ASSERT(
-        popped == context_, "expected popped context to be the current ctx");
-    ev.block(c10::cuda::getStreamFromExternal(parent_stream_, device_id_));
-#else
-    TORCH_CHECK(false, "Green Context is only supported on CUDA 12.8+!");
-#endif
-  }
-} // namespace at::cuda

aten/src/ATen/cuda/CUDAGreenContext.h

@@ -1,53 +0,0 @@
-#pragma once
-#include <ATen/cuda/CUDAEvent.h>
-
-#if defined(CUDA_VERSION) && !defined(USE_ROCM) && defined(PYTORCH_C10_DRIVER_API_SUPPORTED)
-#include <c10/cuda/driver_api.h>
-#include <cuda.h>
-#include <memory>
-#include <stdexcept>
-#include <vector>
-#define CUDA_HAS_GREEN_CONTEXT 1
-#else
-#define CUDA_HAS_GREEN_CONTEXT 0
-#endif
-
-namespace at::cuda {
-
-class TORCH_CUDA_CPP_API GreenContext {
- public:
-  GreenContext(uint32_t device_id, uint32_t num_sms);
-
-  static std::unique_ptr<GreenContext> create(uint32_t num_sms, std::optional<uint32_t> device_id);
-
-  // Delete copy constructor and assignment
-  GreenContext(const GreenContext&) = delete;
-  GreenContext& operator=(const GreenContext&) = delete;
-
-  // Implement move operations
-  GreenContext(GreenContext&& other) noexcept;
-  GreenContext& operator=(GreenContext&& other) noexcept;
-  ~GreenContext() noexcept;
-
-  // Get the underlying CUDA context
-  CUcontext getContext() const;
-
-  // Get the underlying green context
-#if CUDA_HAS_GREEN_CONTEXT
-  CUgreenCtx getGreenContext() const;
-#endif
-
-  // Make this context current
-  void setContext();
-
-  void popContext();
-
- private:
-#if CUDA_HAS_GREEN_CONTEXT
-  int32_t device_id_ = -1;
-  CUgreenCtx green_ctx_ = nullptr;
-  CUcontext context_ = nullptr;
-  cudaStream_t parent_stream_ = nullptr;
-#endif
-};
-} // namespace at::cuda

aten/src/ATen/cuda/CachingHostAllocator.cpp

@@ -183,6 +183,11 @@ struct CUDACachingHostAllocatorImpl
     return true;
   }
 
+  bool pinned_use_background_threads() override {
+    return c10::cuda::CUDACachingAllocator::CUDAAllocatorConfig::
+        pinned_use_background_threads();
+  }
+
   EventPool::Event create_event_internal(DeviceIndex idx) {
     // Leak the event pool to avoid shutdown issue.
     static auto* event_pool = new EventPool();

aten/src/ATen/cuda/cub.cuh

@@ -70,7 +70,11 @@
 #define ATEN_CUB_MAXIMUM() NO_ROCM(at_cuda_detail)ROCM_HIPCUB(::cub)::Max()
 #endif
 
-#if defined(USE_ROCM)
+#if (!defined(USE_ROCM) && !CUB_SUPPORTS_NV_BFLOAT16()) || defined(USE_ROCM)
+
+#if !defined(USE_ROCM)
+namespace at_cuda_detail {
+#endif
 
 // backport https://github.com/NVIDIA/cub/pull/306 for c10::BFloat16
 
@@ -92,6 +96,10 @@ template <>
 struct ROCM_HIPCUB(cub)::NumericTraits<c10::BFloat16>:
        ROCM_HIPCUB(cub)::BaseTraits<ROCM_HIPCUB(cub)::FLOATING_POINT, true, false, unsigned short, c10::BFloat16> {};
 
+#if !defined(USE_ROCM)
+} // namespace at_cuda_detail
+#endif
+
 #endif
 
 #if !defined(USE_ROCM)
@@ -113,7 +121,7 @@ struct cuda_type<c10::Half> {
   using type = __half;
 };
 
-#if !defined(USE_ROCM)
+#if !defined(USE_ROCM) && CUB_SUPPORTS_NV_BFLOAT16()
 
 template<>
 struct cuda_type<c10::BFloat16> {
@@ -169,6 +177,7 @@ inline void segmented_sort_pairs(
   }
 }
 
+#if CUB_SUPPORTS_UNIQUE_BY_KEY()
 template <typename KeysInputIteratorT, typename ValuesInputIteratorT, typename ValuesOutputIteratorT, typename NumSelectedIteratorT>
 inline void unique_by_key(
   KeysInputIteratorT keys_in, ValuesInputIteratorT values_in,
@@ -184,6 +193,7 @@ inline void unique_by_key(
   CUB_WRAPPER(NO_ROCM(at_cuda_detail)::cub::DeviceSelect::UniqueByKey,
     keys_in, values_in, keys_out_, values_out, num_selected, num_input_items, c10::cuda::getCurrentCUDAStream());
 }
+#endif
 
 namespace impl {
 
@@ -195,6 +205,36 @@ __global__ void transform_vals(InputIteratorT1 a, InputIteratorT2 b, OutputItera
   *out = scan_op(static_cast<acc_t>(*a), static_cast<acc_t>(*b));
 }
 
+#if !CUB_SUPPORTS_FUTURE_VALUE()
+template<typename ValueT, typename InputIteratorT>
+struct chained_iterator {
+  using iterator_category = std::random_access_iterator_tag;
+  using difference_type   = std::ptrdiff_t;
+  using value_type        = ValueT;
+  using pointer           = ValueT*;
+  using reference         = ValueT&;
+
+  InputIteratorT iter;
+  ValueT *first;
+  difference_type offset = 0;
+
+  __device__ ValueT operator[](difference_type i) {
+    i +=  offset;
+    if (i == 0) {
+      return *first;
+    } else {
+      return ValueT(iter[i - 1]);
+    }
+  }
+  __device__ chained_iterator operator+(difference_type i) {
+    return chained_iterator{iter, first, i};
+  }
+  __device__ ValueT operator*() {
+    return (*this)[0];
+  }
+};
+#endif
+
 // even though cub is supposed to support tensors with int_max elements, in reality it doesn't,
 // so split at int_max/2
 constexpr int max_cub_size = std::numeric_limits<int>::max() / 2 + 1; // 2**30
@@ -239,6 +279,25 @@ inline void inclusive_scan(InputIteratorT input, OutputIteratorT output, ScanOpT
         first_elem_ptr,
         scan_op);
     C10_CUDA_KERNEL_LAUNCH_CHECK();
+#if !CUB_SUPPORTS_FUTURE_VALUE()
+    using ArgIndexInputIterator = NO_ROCM(at_cuda_detail)::cub::ArgIndexInputIterator<InputIteratorT>;
+    using tuple = typename ArgIndexInputIterator::value_type;
+    auto input_iter_transform = [=] __device__ (const tuple &x)->input_t  {
+      if (x.key == 0) {
+        return *first_elem_ptr;
+      } else {
+        return x.value;
+      }
+    };
+    auto input_ = ATEN_CUB_TRANSFORM_ITERATOR(input_t, decltype(input_iter_transform), ArgIndexInputIterator)(
+      ArgIndexInputIterator(input + i), input_iter_transform);
+    CUB_WRAPPER(NO_ROCM(at_cuda_detail)::cub::DeviceScan::InclusiveScan,
+        input_,
+        output + i,
+        scan_op,
+        size_cub,
+        at::cuda::getCurrentCUDAStream());
+#else
     CUB_WRAPPER(NO_ROCM(at_cuda_detail)::cub::DeviceScan::ExclusiveScan,
         input + i + 1,
         output + i,
@@ -246,6 +305,7 @@ inline void inclusive_scan(InputIteratorT input, OutputIteratorT output, ScanOpT
         ::at_cuda_detail::cub::FutureValue<input_t>(first_elem_ptr),
         size_cub,
         at::cuda::getCurrentCUDAStream());
+#endif
   }
 #endif
 }
@@ -497,6 +557,16 @@ inline void exclusive_scan(InputIteratorT input, OutputIteratorT output, ScanOpT
         first_elem_ptr,
         scan_op);
     C10_CUDA_KERNEL_LAUNCH_CHECK();
+#if !CUB_SUPPORTS_FUTURE_VALUE()
+    auto input_ = impl::chained_iterator<InitValueT, InputIteratorT>{
+      input + i, first_elem_ptr};
+    CUB_WRAPPER(NO_ROCM(at_cuda_detail)::cub::DeviceScan::InclusiveScan,
+        input_,
+        output + i,
+        scan_op,
+        size_cub,
+        at::cuda::getCurrentCUDAStream());
+#else
     CUB_WRAPPER(NO_ROCM(at_cuda_detail)::cub::DeviceScan::ExclusiveScan,
         input + i,
         output + i,
@@ -504,10 +574,12 @@ inline void exclusive_scan(InputIteratorT input, OutputIteratorT output, ScanOpT
         ::at_cuda_detail::cub::FutureValue<InitValueT>(first_elem_ptr),
         size_cub,
         at::cuda::getCurrentCUDAStream());
+#endif
   }
 #endif
 }
 
+#if CUB_SUPPORTS_SCAN_BY_KEY()
 
 template <typename KeysInputIteratorT, typename ValuesInputIteratorT, typename ValuesOutputIteratorT>
 inline void inclusive_sum_by_key(KeysInputIteratorT keys, ValuesInputIteratorT input, ValuesOutputIteratorT output, int64_t num_items) {
@@ -535,6 +607,7 @@ inline void inclusive_scan_by_key(KeysInputIteratorT keys, ValuesInputIteratorT
 #endif
 }
 
+#endif
 
 template <typename InputIteratorT, typename OutputIteratorT, typename NumSelectedIteratorT>
 void unique(InputIteratorT input, OutputIteratorT output,

aten/src/ATen/cuda/cub_definitions.cuh

@@ -10,6 +10,14 @@
 #define CUB_VERSION 200001
 #endif
 
+// cub sort support for __nv_bfloat16 is added to cub 1.13 in:
+// https://github.com/NVIDIA/cub/pull/306
+#if CUB_VERSION >= 101300
+#define CUB_SUPPORTS_NV_BFLOAT16() true
+#else
+#define CUB_SUPPORTS_NV_BFLOAT16() false
+#endif
+
 // cub support for CUB_WRAPPED_NAMESPACE is added to cub 1.13.1 in:
 // https://github.com/NVIDIA/cub/pull/326
 // CUB_WRAPPED_NAMESPACE is defined globally in cmake/Dependencies.cmake
@@ -20,6 +28,30 @@
 #define USE_GLOBAL_CUB_WRAPPED_NAMESPACE() false
 #endif
 
+// cub support for UniqueByKey is added to cub 1.16 in:
+// https://github.com/NVIDIA/cub/pull/405
+#if CUB_VERSION >= 101600
+#define CUB_SUPPORTS_UNIQUE_BY_KEY() true
+#else
+#define CUB_SUPPORTS_UNIQUE_BY_KEY() false
+#endif
+
+// cub support for scan by key is added to cub 1.15
+// in https://github.com/NVIDIA/cub/pull/376
+#if CUB_VERSION >= 101500
+#define CUB_SUPPORTS_SCAN_BY_KEY() 1
+#else
+#define CUB_SUPPORTS_SCAN_BY_KEY() 0
+#endif
+
+// cub support for cub::FutureValue is added to cub 1.15 in:
+// https://github.com/NVIDIA/cub/pull/305
+#if CUB_VERSION >= 101500
+#define CUB_SUPPORTS_FUTURE_VALUE() true
+#else
+#define CUB_SUPPORTS_FUTURE_VALUE() false
+#endif
+
 // There were many bc-breaking changes in major version release of CCCL v3.0.0
 // Please see https://nvidia.github.io/cccl/cccl/3.0_migration_guide.html
 #if CUB_VERSION >= 200800

aten/src/ATen/detail/XLAHooksInterface.cpp

@@ -1,23 +0,0 @@
-#include <ATen/detail/XLAHooksInterface.h>
-
-namespace at {
-namespace detail {
-
-const XLAHooksInterface& getXLAHooks() {
-  auto create_impl = [] {
-    // Create XLA hooks using the registry
-    auto hooks = XLAHooksRegistry()->Create("torch_xla::detail::XLAHooks", XLAHooksArgs{});
-    if (hooks) {
-      return hooks;
-    }
-    // If hooks creation fails, fall back to default implementation
-    return std::make_unique<XLAHooksInterface>();
-  };
-  static auto hooks = create_impl();
-  return *hooks;
-}
-} // namespace detail
-
-C10_DEFINE_REGISTRY(XLAHooksRegistry, XLAHooksInterface, XLAHooksArgs)
-
-} // namespace at

aten/src/ATen/detail/XLAHooksInterface.h

@@ -1,79 +0,0 @@
-#pragma once
-
-#include <c10/core/Device.h>
-#include <c10/util/Exception.h>
-#include <c10/util/Registry.h>
-
-#include <ATen/detail/AcceleratorHooksInterface.h>
-
-C10_DIAGNOSTIC_PUSH_AND_IGNORED_IF_DEFINED("-Wunused-parameter")
-
-namespace at {
-
-constexpr const char* XLA_HELP =
-  "This error has occurred because you are trying "
-  "to use some XLA functionality, but the XLA library has not been "
-  "loaded by the dynamic linker. You must load xla libraries by `import torch_xla`";
-
-struct TORCH_API XLAHooksInterface : AcceleratorHooksInterface {
-  ~XLAHooksInterface() override = default;
-
-  void init() const override {
-    TORCH_CHECK(false, "Cannot initialize XLA without torch_xla library. ", XLA_HELP);
-  }
-
-  virtual bool hasXLA() const {
-    return false;
-  }
-
-  virtual std::string showConfig() const {
-    TORCH_CHECK(
-        false,
-        "Cannot query detailed XLA version without torch_xla library. ",
-        XLA_HELP);
-  }
-
-  const Generator& getDefaultGenerator(
-      [[maybe_unused]] DeviceIndex device_index = -1) const override {
-    TORCH_CHECK(
-        false, "Cannot get default XLA generator without torch_xla library. ", XLA_HELP);
-  }
-
-  Generator getNewGenerator(
-      [[maybe_unused]] DeviceIndex device_index = -1) const override {
-    TORCH_CHECK(false, "Cannot get XLA generator without torch_xla library. ", XLA_HELP);
-  }
-
-  virtual DeviceIndex getCurrentDevice() const override {
-    TORCH_CHECK(false, "Cannot get current XLA device without torch_xla library. ", XLA_HELP);
-  }
-
-  Device getDeviceFromPtr(void* /*data*/) const override {
-    TORCH_CHECK(false, "Cannot get device of pointer on XLA without torch_xla library. ", XLA_HELP);
-  }
-
-  Allocator* getPinnedMemoryAllocator() const override {
-    TORCH_CHECK(false, "Cannot get XLA pinned memory allocator without torch_xla library. ", XLA_HELP);
-  }
-
-  bool isPinnedPtr(const void* data) const override {
-    return false;
-  }
-
-  bool hasPrimaryContext(DeviceIndex device_index) const override {
-    TORCH_CHECK(false, "Cannot query primary context without torch_xla library. ", XLA_HELP);
-  }
-
-};
-
-struct TORCH_API XLAHooksArgs {};
-
-TORCH_DECLARE_REGISTRY(XLAHooksRegistry, XLAHooksInterface, XLAHooksArgs);
-#define REGISTER_XLA_HOOKS(clsname) \
-  C10_REGISTER_CLASS(XLAHooksRegistry, clsname, clsname)
-
-namespace detail {
-TORCH_API const XLAHooksInterface& getXLAHooks();
-} // namespace detail
-} // namespace at
-C10_DIAGNOSTIC_POP()

aten/src/ATen/functorch/BatchedTensorImpl.h

@@ -160,10 +160,6 @@ constexpr DispatchKeySet kKeysToPropagateToWrapper({
   DispatchKey::CUDA,
   DispatchKey::CPU,
   DispatchKey::PrivateUse1,
-  DispatchKey::SparseCPU,
-  DispatchKey::SparseCUDA,
-  DispatchKey::SparseCsrCPU,
-  DispatchKey::SparseCsrCUDA,
 });
 
 inline DispatchKeySet getKeysToPropagateToWrapper(const Tensor& tensor, DispatchKeySet to_propagate=kKeysToPropagateToWrapper) {

aten/src/ATen/native/Convolution.cpp

@@ -658,7 +658,6 @@ static void check_shape_forward(const at::Tensor& input,
   TORCH_CHECK(!params.is_output_padding_neg(), "negative output_padding is not supported");
   TORCH_CHECK(!params.is_stride_nonpos(), "non-positive stride is not supported");
   TORCH_CHECK(!params.is_dilation_neg(), "dilation should be greater than zero");
-  TORCH_CHECK(groups > 0, "expected groups to be greater than 0, but got groups=", groups);
 
   TORCH_CHECK(weight_dim == k,
            "Expected ", weight_dim, "-dimensional input for ", weight_dim,

aten/src/ATen/native/LinearAlgebra.cpp

@@ -3620,7 +3620,7 @@ Tensor& _int_mm_out_cpu(const Tensor& self, const Tensor& mat2, Tensor& result)
     try {
       mkldnn_matmul_i8i8i32(self, mat2, result);
       dispatched = true;
-    } catch ([[maybe_unused]] const std::exception& e) {
+    } catch (const std::exception& e) {
       TORCH_WARN(func_name, " failed, switching to BLAS gemm: ", e.what());
     }
   }

aten/src/ATen/native/PixelShuffle.h

@@ -11,8 +11,6 @@ inline void check_pixel_shuffle_shapes(const Tensor& self, int64_t upscale_facto
               "pixel_shuffle expects a positive upscale_factor, but got ",
               upscale_factor);
   int64_t c = self.size(-3);
-  TORCH_CHECK_VALUE(upscale_factor <= std::numeric_limits<decltype(upscale_factor)>::max() / upscale_factor,
-        "upscale factor is too large, (upscale_factor)^2 overflowed: upscale_factor=", upscale_factor);
   int64_t upscale_factor_squared = upscale_factor * upscale_factor;
   TORCH_CHECK(c % upscale_factor_squared == 0,
               "pixel_shuffle expects its input's 'channel' dimension to be divisible by the square of "

aten/src/ATen/native/cpu/DepthwiseConvKernel.cpp

@@ -259,20 +259,11 @@ inline void winograd_f2k3_input_transform_inplace__rvv(
   const vfloat32m1_t wd1 = __riscv_vfadd_vv_f32m1(d1, d2, 4);
   const vfloat32m1_t wd2 = __riscv_vfsub_vv_f32m1(d2, d1, 4);
   const vfloat32m1_t wd3 = __riscv_vfsub_vv_f32m1(d1, d3, 4);
-  /* GCC 14.2 (RISC-V RVV) ICE workaround:
-   * Avoid single-statement read-modify-write on MEM_REF like:
-   *   *input_tile_val =
-   *     __riscv_vset_v_f32m1_f32m1x4(*input_tile_val, idx, val);
-   * This triggers an ICE during GIMPLE lower (gsi_replace / riscv_gimple_fold_builtin)
-   * with -march=rv64gcv. Use a temporary then write back.
-   * Do NOT refactor into the single-statement form. Clang is unaffected.
-   */
-  vfloat32m1x4_t tmp_input_tile_val = *input_tile_val;
-  tmp_input_tile_val = __riscv_vset_v_f32m1_f32m1x4(tmp_input_tile_val, 0, wd0);
-  tmp_input_tile_val = __riscv_vset_v_f32m1_f32m1x4(tmp_input_tile_val, 1, wd1);
-  tmp_input_tile_val = __riscv_vset_v_f32m1_f32m1x4(tmp_input_tile_val, 2, wd2);
-  tmp_input_tile_val = __riscv_vset_v_f32m1_f32m1x4(tmp_input_tile_val, 3, wd3);
-  *input_tile_val = tmp_input_tile_val;
+
+  *input_tile_val = __riscv_vset_v_f32m1_f32m1x4(*input_tile_val, 0, wd0);
+  *input_tile_val = __riscv_vset_v_f32m1_f32m1x4(*input_tile_val, 1, wd1);
+  *input_tile_val = __riscv_vset_v_f32m1_f32m1x4(*input_tile_val, 2, wd2);
+  *input_tile_val = __riscv_vset_v_f32m1_f32m1x4(*input_tile_val, 3, wd3);
 }
 
 inline void winograd_f2k3_output_transform_inplace__rvv(
@@ -286,15 +277,9 @@ inline void winograd_f2k3_output_transform_inplace__rvv(
   const vfloat32m1_t wm0 = __riscv_vfadd_vv_f32m1(m0_plus_m1, m2, 4);
   const vfloat32m1_t m1_sub_m2 = __riscv_vfsub_vv_f32m1(m1, m2, 4);
   const vfloat32m1_t wm1 = __riscv_vfsub_vv_f32m1(m1_sub_m2, m3, 4);
-  /* GCC 14.2 (RISC-V RVV) ICE workaround — see note above.
-   * Keep the temporary + write-back pattern to avoid ICE.
-   * Do NOT rewrite into:
-   *   *input_tile_val = __riscv_vset_v_f32m1_f32m1x4(*input_tile_val, idx, val);
-   */
-  vfloat32m1x4_t tmp_output_tile_val = *input_tile_val;
-  tmp_output_tile_val = __riscv_vset_v_f32m1_f32m1x4(tmp_output_tile_val, 0, wm0);
-  tmp_output_tile_val = __riscv_vset_v_f32m1_f32m1x4(tmp_output_tile_val, 1, wm1);
-  *input_tile_val = tmp_output_tile_val;
+
+  *input_tile_val = __riscv_vset_v_f32m1_f32m1x4(*input_tile_val, 0, wm0);
+  *input_tile_val = __riscv_vset_v_f32m1_f32m1x4(*input_tile_val, 1, wm1);
 }
 
 inline vfloat32m1_t
@@ -315,17 +300,11 @@ inline void winograd_f2k3_kernel_transform__rvv(
   const vfloat32m1_t const_half = __riscv_vfmv_v_f_f32m1(0.5f, 4);
   const vfloat32m1_t g0_plus_g2 = __riscv_vfadd_vv_f32m1(g0, g2, 4);
   vfloat32m1_t half_g0_plus_g2 =  __riscv_vfmul_vv_f32m1(const_half, g0_plus_g2, 4);
-  /* GCC 14.2 (RISC-V RVV) ICE workaround — see note above.
-   * Keep the temporary + write-back pattern to avoid ICE.
-   * Do NOT rewrite into:
-   *   *transform = __riscv_vset_v_f32m1_f32m1x4(*transform, idx, val);
-   */
-  vfloat32m1x4_t tmp_transform = *transform;
-  tmp_transform = __riscv_vset_v_f32m1_f32m1x4(tmp_transform, 0, g0);
-  tmp_transform = __riscv_vset_v_f32m1_f32m1x4(tmp_transform, 1, vmuladdq_f32(half_g0_plus_g2, const_half, g1));
-  tmp_transform = __riscv_vset_v_f32m1_f32m1x4(tmp_transform, 2, vmulsubq_f32(half_g0_plus_g2, const_half, g1));
-  tmp_transform = __riscv_vset_v_f32m1_f32m1x4(tmp_transform, 3, g2);
-  *transform = tmp_transform;
+
+  *transform = __riscv_vset_v_f32m1_f32m1x4(*transform, 0, g0);
+  *transform = __riscv_vset_v_f32m1_f32m1x4(*transform, 1, vmuladdq_f32(half_g0_plus_g2, const_half, g1));
+  *transform = __riscv_vset_v_f32m1_f32m1x4(*transform, 2, vmulsubq_f32(half_g0_plus_g2, const_half, g1));
+  *transform = __riscv_vset_v_f32m1_f32m1x4(*transform, 3, g2);
 }
 
 inline vfloat32m1x4_t v4f_transpose4x4__rvv(const vfloat32m1x4_t m) {

aten/src/ATen/native/cuda/Blas.cpp

@@ -272,110 +272,28 @@ cuda::blas::GEMMAndBiasActivationEpilogue activation_to_gemm_and_blas_arg(Activa
   }
 }
 
-/*
- * Checks whether DISABLE_ADDMM_CUDA_LT is set.
- * Additionally, for ROCM we test whether the architecture supports the Lt.
- */
-static bool isGloballyDisabledAddmmCudaLt(const at::Device& device) {
-  // When hipBLASLt is not supported on the architecture, return true
-  #ifdef USE_ROCM
-  static const std::vector<std::string> archs = {
+static bool getDisableAddmmCudaLt() {
+    static const auto env_value = c10::utils::get_env("DISABLE_ADDMM_CUDA_LT");
+    if (env_value == "1") {
+      return true;
+    }
+    return false;
+}
+
+#ifdef USE_ROCM
+static bool isSupportedHipLtROCmArch(int index) {
+    static const std::vector<std::string> archs = {
         "gfx90a", "gfx942",
-    #if ROCM_VERSION >= 60300
+#if ROCM_VERSION >= 60300
         "gfx1100", "gfx1101", "gfx1200", "gfx1201", "gfx908",
-    #endif
-    #if ROCM_VERSION >= 70000
+#endif
+#if ROCM_VERSION >= 70000
         "gfx950", "gfx1150", "gfx1151"
-    #endif
-  };
-  const auto is_hipblas_lt_arch_supported = at::detail::getCUDAHooks().isGPUArch(archs, device.index());
-  if (!is_hipblas_lt_arch_supported) {
-    return true;
-  }
-  #endif
-
-  // Check whether it is disabled in the env
-  static const auto is_addmm_cuda_lt_disabled = c10::utils::get_env("DISABLE_ADDMM_CUDA_LT");
-  if (is_addmm_cuda_lt_disabled == "1") {
-    return true;
-  }
-
-  return false;
-}
-
-/*
- * Check whether for the given input we want to enable the Lt interface
- */
-static bool isInputCompliesAddmmCudaLt(Tensor& result, const Tensor& self, const Tensor& mat1, const Tensor& mat2, const Scalar& beta, const Scalar& alpha) {
-  // Implies 2D bias which we currently not send through Lt.
-  // TODO: this check is done pre col-major input preparation,
-  // so, this condition can be ralexed in cases when a col-major
-  // copy of result is needed.
-  if (result.is_same(self)) {
-    return false;
-  }
-
-  #if defined(USE_ROCM) && ROCM_VERSION == 60400
-  // hipblaslt TT fp32 regression on ROCm 6.4, cannot use
-  const auto args = cublasCommonArgs(mat1, mat2, result);
-  if (args.transa == 't' && args.transb == 't') {
-    return false;
-  }
-  #endif
-
-  const auto mat1_sizes = mat1.sizes();
-  const auto mat2_sizes = mat2.sizes();
-  #if defined(CUDA_VERSION) || defined(USE_ROCM)
-  const auto scalar_type = mat1.scalar_type();
-  return (beta.toComplexDouble() == 1.0
-    // self.dim() == 1 && result.dim() == 2 && self.sizes()[0] == mat2_sizes[1]
-    // is to use lt interface only when self is bias.
-    && self.dim() == 1 && self.sizes()[0] == mat2_sizes[1] && self.is_contiguous()
-    && result.dim() == 2 && result.is_contiguous()
-    && ( // some dtype restrictions
-      #ifndef USE_ROCM
-      scalar_type == at::ScalarType::Double ||
-      #endif
-      scalar_type == at::ScalarType::Float ||
-      scalar_type == at::ScalarType::Half ||
-      scalar_type == at::ScalarType::BFloat16
-    )
-    && ( // some shape/stride restrictions
-      // Strangely, if mat2 has only 1 row or column, we get
-      // CUBLAS_STATUS_INVALID_VALUE error from cublasLtMatmulAlgoGetHeuristic.
-      // NOTE: extension to mat1 because mat1/mat2 can be swapped based off
-      // their row-/col-majorness.
-      mat1_sizes[0] > 1 && mat1_sizes[1] > 1 &&
-      mat2_sizes[0] > 1 && mat2_sizes[1] > 1
-      // The last conditions is to skip 16b transA and non-trans-B having
-      // leading dim >> rows when they are sliced from a large tensor
-      // see fbcode/caffe2/test/test_linalg.py:test_corner_cases_of_cublasltmatmul
-      #if !(defined(CUDA_VERSION) && CUDA_VERSION >= 12010 || defined(USE_ROCM))
-      // Related to avoiding the leading stride >> leading dim problematic case
-      // with 16b dtypes described above. For such dtypes we only allow inputs
-      // which are either row- or col-major (i.e. non-overlapping, compact memory layout).
-      // In that case the leading stride will be equal to the outer dim len.
-      // Why do we catch this case here? The following `prepare_matrix_for_cublas` method
-      // does not modify inputs as long as there is a stride of length 1
-      // and the leading stride is at least max(1, other dim length), so we might
-      // end up with contiguous cols but not rows (i.e. holes between different rows)
-      // and vice versa.
-      && mat2_sizes[0] < 65535 * 32 && mat2_sizes[1] < 65535 * 32 &&
-      mat1_sizes[0] < 65535 * 32 && mat1_sizes[1] < 65535 * 32 &&
-      && (
-        // filter by dtype
-        (scalar_type != at::ScalarType::Half && scalar_type != at::ScalarType::BFloat16) ||
-        // check mat1/mat2 is row-/col-major
-        (mat1.is_non_overlapping_and_dense() && mat2.is_non_overlapping_and_dense())
-      )
-      #endif
-    )
-  );
-  #endif
-
-  // no compliance by default
-  return false;
+#endif
+    };
+    return at::detail::getCUDAHooks().isGPUArch(archs, index);
 }
+#endif
 
 template <typename scalar_t>
 void launchTunableGemmAndBias(cublasCommonArgs &args, const Scalar& alpha, const scalar_t* bias, cuda::blas::GEMMAndBiasActivationEpilogue activation) {
@@ -417,70 +335,7 @@ void launchTunableGemmAndBias(cublasCommonArgs &args, const Scalar& alpha, const
   }
 }
 
-template <typename scalar_t, typename res_scalar_t = scalar_t>
-bool launchGemmAndBiasCublasLt(
-    // args contains result which is modified
-    cublasCommonArgs& args,
-    const Tensor& self,
-    const Scalar& alpha,
-    Activation activation = Activation::None
-) {
-  const auto* self_ptr = self.const_data_ptr<scalar_t>();
-
-  const auto tuning_ctx = at::cuda::tunable::getTuningContext();
-  if (tuning_ctx->IsTunableOpEnabled()) {
-    // TODO: maybe also return some success state?
-    launchTunableGemmAndBias<scalar_t>(
-      args, alpha, self_ptr, activation_to_gemm_and_blas_arg(activation)
-    );
-    return true;
-  }
-
-  return at::cuda::blas::gemm_and_bias<scalar_t, res_scalar_t>(
-    args.transa == 't',
-    args.transb == 't',
-    args.m,
-    args.n,
-    args.k,
-    alpha.to<at::opmath_type<scalar_t>>(),
-    args.mata->const_data_ptr<scalar_t>(),
-    args.lda,
-    args.matb->const_data_ptr<scalar_t>(),
-    args.ldb,
-    self_ptr,
-    args.result->data_ptr<res_scalar_t>(),
-    args.result_ld,
-    activation_to_gemm_and_blas_arg(activation)
-  );
-}
-
-template <typename scalar_t, typename res_scalar_t = scalar_t>
-bool launchGemmCublas(
-    // args contains result which is modified
-    cublasCommonArgs& args,
-    const Scalar& alpha,
-    const Scalar& beta
-) {
-  at::cuda::blas::gemm<scalar_t, res_scalar_t>(
-    args.transa,
-    args.transb,
-    args.m,
-    args.n,
-    args.k,
-    alpha.to<at::opmath_type<scalar_t>>(),
-    args.mata->const_data_ptr<scalar_t>(),
-    args.lda,
-    args.matb->const_data_ptr<scalar_t>(),
-    args.ldb,
-    beta.to<at::opmath_type<scalar_t>>(),
-    args.result->data_ptr<res_scalar_t>(),
-    args.result_ld
-  );
-  return true; // success!
-}
-
 Tensor& addmm_out_cuda_impl(Tensor& result, const Tensor& self, const Tensor& mat1, const Tensor& mat2, const Scalar& beta, const Scalar& alpha, Activation activation=Activation::None, bool disable_addmm_cuda_lt_override=false) {
-  // Shape checks {
   // Make sure to keep addmm_cuda below in sync with this code; it
   // preflights a check to try to avoid actually needing to call
   // expand().
@@ -490,62 +345,105 @@ Tensor& addmm_out_cuda_impl(Tensor& result, const Tensor& self, const Tensor& ma
     "expected mat1 and mat2 to have the same dtype, but got: ", mat1.dtype(), " != ", mat2.dtype()
   )
 
-  if (result.is_same(self)) {
-    TORCH_CHECK(result.dim() == 2, "tensors must be 2-D");
-    TORCH_CHECK(self.sizes()[0] == mat1.sizes()[0], "self dim 0 must match mat1 dim 0");
-    TORCH_CHECK(self.sizes()[1] == mat2.sizes()[1], "self dim 1 must match mat2 dim 1");
-  }
-  // } Shape checks
-
   // NOLINTNEXTLINE(*c-array*)
   TensorArg targs[]{{result, "out", 0}, {self, "self", 1}, {mat1, "mat1", 2}, {mat2, "mat2", 3}};
   checkAllSameGPU(__func__, targs);
 
-  // Handle whether to use the Lt interface {
-  static bool persistent_disable_addmm_cuda_lt = isGloballyDisabledAddmmCudaLt(self.device());
+  IntArrayRef mat1_sizes = mat1.sizes();
+  IntArrayRef mat2_sizes = mat2.sizes();
+  IntArrayRef self__sizes;
+  bool useLtInterface = false;
+#if defined(USE_ROCM)
+  // When hipBLASLt is not supported on the architecture,
+  // disable_addmm_cuda_lt will always be to set to true
+  static bool disable_addmm_cuda_lt =
+    !isSupportedHipLtROCmArch(self.device().index()) || getDisableAddmmCudaLt();
+#else
+  static bool disable_addmm_cuda_lt = getDisableAddmmCudaLt();
+#endif
   // if lt path fails, we recurse back into this function here and force the lt path to off
   // we cannot update varible disable_addmm_cuda_lt from above since it is static and would be permanent
-  bool disable_addmm_cuda_lt = persistent_disable_addmm_cuda_lt || disable_addmm_cuda_lt_override;
-  #ifdef USE_ROCM
-  // Conditioned on the device index, which is not persistent
-  disable_addmm_cuda_lt = isGloballyDisabledAddmmCudaLt(self.device()) || disable_addmm_cuda_lt;
-  #endif
-  // Condition on the input
-  disable_addmm_cuda_lt = !isInputCompliesAddmmCudaLt(result, self, mat1, mat2, beta, alpha) || disable_addmm_cuda_lt;
-  // }
-
+  bool disable_addmm_cuda_lt_final = disable_addmm_cuda_lt || disable_addmm_cuda_lt_override;
+#if defined(USE_ROCM) && ROCM_VERSION == 60400
+  // hipblaslt TT fp32 regression on ROCm 6.4, cannot use
+  cublasCommonArgs _args(mat1, mat2, result);
+  if (_args.transa == 't' && _args.transb == 't') {
+    disable_addmm_cuda_lt_final = true;
+  }
+#endif
   at::ScalarType scalar_type = mat1.scalar_type();
   bool is_float_output_with_half_input = (scalar_type == at::ScalarType::Half || scalar_type == at::ScalarType::BFloat16) && result.scalar_type() == at::ScalarType::Float;
+  c10::MaybeOwned<Tensor> self_;
+  if (&result != &self) {
+#if defined(CUDA_VERSION) || defined(USE_ROCM)
+    // Strangely, if mat2 has only 1 row or column, we get
+    // CUBLAS_STATUS_INVALID_VALUE error from cublasLtMatmulAlgoGetHeuristic.
+    // self.dim() == 1 && result.dim() == 2 && self.sizes()[0] == mat2_sizes[1]
+    // is to use lt interface only when self is bias.
+    // for cuda 11.4, cublasLtMatmul is activated
+    // the last two conditions is to skip 16b transA and non-trans-B having
+    // leading dim >> rows when they are sliced from a large tensor
+    // see fbcode/caffe2/test/test_linalg.py:test_corner_cases_of_cublasltmatmul
+    if (!disable_addmm_cuda_lt_final) {
+      useLtInterface = beta.toComplexDouble() == 1.0 && self.dim() == 1 &&
+          result.dim() == 2 && self.sizes()[0] == mat2_sizes[1] &&
+          self.is_contiguous() && result.is_contiguous() &&
+#ifdef USE_ROCM
+          (scalar_type == at::ScalarType::Float ||
+           scalar_type == at::ScalarType::Half ||
+           scalar_type == at::ScalarType::BFloat16) &&
+#else
+          (scalar_type == at::ScalarType::Double ||
+           scalar_type == at::ScalarType::Float ||
+           scalar_type == at::ScalarType::Half ||
+           scalar_type == at::ScalarType::BFloat16) &&
+#endif
+#if (defined(CUDA_VERSION) && CUDA_VERSION >= 12010 || defined(USE_ROCM))
+          mat2_sizes[0] > 1 && mat2_sizes[1] > 1;
+#else
+          mat2_sizes[0] > 1 && mat2_sizes[1] > 1 &&
+          mat2_sizes[0] < 65535 * 32 && mat2_sizes[1] < 65535 * 32 &&
+          mat1_sizes[0] < 65535 * 32 && mat1_sizes[1] < 65535 * 32 &&
+          // avoid leading dim >> rows bugs
+          ((mat1.strides()[0] == 1 && mat1.strides()[1] == mat1_sizes[0]) ||
+           (mat1.strides()[1] == 1 && mat1.strides()[0] == mat1_sizes[1]) ||
+           (scalar_type != at::ScalarType::Half &&
+            scalar_type != at::ScalarType::BFloat16)) &&
+          ((mat2.strides()[0] == 1 && mat2.strides()[1] == mat2_sizes[0]) ||
+           (mat2.strides()[1] == 1 && mat2.strides()[0] == mat2_sizes[1]) ||
+           (scalar_type != at::ScalarType::Half &&
+            scalar_type != at::ScalarType::BFloat16));
+#endif
+    }
+#endif
+    if (!useLtInterface) {
+      self_ = expand_size(self, {mat1_sizes[0], mat2_sizes[1]}, "addmm");
+    }
+    self__sizes = self_->sizes();
+  } else {
+    self_ = c10::MaybeOwned<Tensor>::borrowed(self);
+    self__sizes = self_->sizes();
+    TORCH_CHECK(result.dim() == 2, "tensors must be 2-D");
+    TORCH_CHECK(self__sizes[0] == mat1_sizes[0], "self_ dim 0 must match mat1 dim 0");
+    TORCH_CHECK(self__sizes[1] == mat2_sizes[1], "self_ dim 1 must match mat2 dim 1");
+  }
 
-  // Handle result/self shapes
-  if (!result.is_same(self)) {
-    at::native::resize_output(result, {mat1.sizes()[0], mat2.sizes()[1]});
-
-    const auto self_maybe_expanded = [&]() -> c10::MaybeOwned<Tensor> {
-      if (disable_addmm_cuda_lt) {
-        // When in non-Lt path we do expand self even before
-        // check for beta != 0.0 to make sure that
-        // test_sparse_csr.py::TestSparseCSRCUDA::test_addmm_errors_*
-        // runs green.
-        return expand_size(self, result.sizes(), "addmm");
-      }
-      // copy next, should broadcast
-      return c10::MaybeOwned<Tensor>::borrowed(self);
-    }();
-    // We copy bias when in the non-Lt path
-    if (beta.toComplexDouble() != 0.0 && disable_addmm_cuda_lt) {
-      // NOTE: self should broadcast over result
-      at::native::copy_(result, *self_maybe_expanded);
+  if (&result != &self) {
+    at::native::resize_output(result, {mat1_sizes[0], mat2_sizes[1]});
+    if (beta.toComplexDouble() != 0.0 && !useLtInterface) {
+      at::native::copy_(result, *self_);
     }
   }
 
-  // Short circuit on empty result
-  if (result.numel() == 0) {
+
+  IntArrayRef result_sizes = result.sizes();
+  if ((result_sizes[0] == 0) || (result_sizes[1] == 0)) {
     return result;
   }
 
-  // Short circuit if the reduction dim is empty
-  if (mat1.sizes()[1] == 0) {
+  cublasCommonArgs args(mat1, mat2, result);
+
+  if (mat1.numel() == 0) {
     // By definition, when beta==0, values in self should be ignored. nans and infs
     // should not propagate
     if (beta.toComplexDouble() == 0.) {
@@ -557,64 +455,158 @@ Tensor& addmm_out_cuda_impl(Tensor& result, const Tensor& self, const Tensor& ma
         result,
         self.expand(result.sizes()),
         at::native::scalar_tensor(
-          beta,
-          self.scalar_type(),
-          std::nullopt /* layout */,
-          at::kCPU,
-          std::nullopt /* pin_memory */
-        )
-    );
+            beta,
+            self.scalar_type(),
+            std::nullopt /* layout */,
+            at::kCPU,
+            std::nullopt /* pin_memory */));
   }
 
-  cublasCommonArgs args(mat1, mat2, result);
   TORCH_INTERNAL_ASSERT_DEBUG_ONLY(!args.result->is_conj());
 
-  // The Lt path
-  if (!disable_addmm_cuda_lt) {
-    bool lt_success = false;
+  if (useLtInterface) {
+#if defined(USE_ROCM)
+    bool okay = true;
     if (is_float_output_with_half_input) {
-      #ifdef USE_ROCM
       TORCH_CHECK(false, "float output with half input is not enabled for ROCm");
-      #else
-      if (at::cuda::tunable::getTuningContext()->IsTunableOpEnabled()) {
-       TORCH_CHECK(false, "Tunable GEMM is not supported for float output with reduced float input");
-      }
-      AT_DISPATCH_REDUCED_FLOATING_TYPES(
-        scalar_type,
-        "addmm_cuda_lt",
-        [&] {
-          lt_success = launchGemmAndBiasCublasLt<scalar_t, float>(args, self, alpha, activation);
-        }
-      );
-      #endif
     } else {
-      // !is_float_output_with_half_input
       AT_DISPATCH_FLOATING_TYPES_AND2(
         at::ScalarType::Half,
         at::ScalarType::BFloat16,
         scalar_type,
         "addmm_cuda_lt",
         [&] {
-          lt_success = launchGemmAndBiasCublasLt<scalar_t>(args, self, alpha, activation);
+        auto tuning_ctx = at::cuda::tunable::getTuningContext();
+        if (tuning_ctx->IsTunableOpEnabled()) {
+          launchTunableGemmAndBias<scalar_t>(
+              args,
+              alpha,
+              (&result != &self) ? self.const_data_ptr<scalar_t>() : nullptr,
+              activation_to_gemm_and_blas_arg(activation));
+        } else {
+          okay = at::cuda::blas::gemm_and_bias<scalar_t>(
+            args.transa == 't',
+            args.transb == 't',
+            args.m,
+            args.n,
+            args.k,
+            alpha.to<at::opmath_type<scalar_t>>(),
+            args.mata->const_data_ptr<scalar_t>(),
+            args.lda,
+            args.matb->const_data_ptr<scalar_t>(),
+            args.ldb,
+            // This condition is needed for mm case on ROCm for hipblasLt path.
+            // Passing the bias ptr as null to avoid accuracy issues for mm case.
+            (&result != &self) ? self.const_data_ptr<scalar_t>() : nullptr,
+            args.result->data_ptr<scalar_t>(),
+            args.result_ld,
+            activation_to_gemm_and_blas_arg(activation)
+          );
         }
-      );
-    } // end is_float_output_with_half_input
-
-    if (!lt_success) {
-    // lt path failed; recurse but disable lt path
+      });
+    }
+    if (!okay) {
+      // lt path failed; recurse but disable lt path
       return addmm_out_cuda_impl(result, self, mat1, mat2, beta, alpha, activation, true);
     }
-    // end Lt path
-  } else {
-    // No Lt, we use a GEMM instead
+#else
+    auto activation_epilogue = activation_to_gemm_and_blas_arg(activation);
+    bool okay = true;
+    if (is_float_output_with_half_input) {
+      AT_DISPATCH_REDUCED_FLOATING_TYPES(
+        scalar_type,
+        "addmm_cuda_lt",
+        [&] {
+        auto tuning_ctx = at::cuda::tunable::getTuningContext();
+        if (tuning_ctx->IsTunableOpEnabled()) {
+          TORCH_CHECK(false, "Tunable GEMM is not supported for float output with reduced float input");
+        }
+        else {
+          okay = at::cuda::blas::gemm_and_bias<scalar_t, float>(
+              args.transa == 't',
+              args.transb == 't',
+              args.m,
+              args.n,
+              args.k,
+              alpha.to<at::opmath_type<scalar_t>>(),
+              args.mata->const_data_ptr<scalar_t>(),
+              args.lda,
+              args.matb->const_data_ptr<scalar_t>(),
+              args.ldb,
+              self.const_data_ptr<scalar_t>(),
+              args.result->data_ptr<float>(),
+              args.result_ld,
+              activation_epilogue
+          );
+        }});
+    } else {
+      AT_DISPATCH_FLOATING_TYPES_AND2(
+        at::ScalarType::Half,
+        at::ScalarType::BFloat16,
+        scalar_type,
+        "addmm_cuda_lt",
+        [&] {
+        auto tuning_ctx = at::cuda::tunable::getTuningContext();
+        if (tuning_ctx->IsTunableOpEnabled()) {
+          launchTunableGemmAndBias<scalar_t>(
+              args,
+              alpha,
+              self.const_data_ptr<scalar_t>(),
+              activation_epilogue);
+        }
+        else {
+          okay = at::cuda::blas::gemm_and_bias<scalar_t>(
+              args.transa == 't',
+              args.transb == 't',
+              args.m,
+              args.n,
+              args.k,
+              alpha.to<at::opmath_type<scalar_t>>(),
+              args.mata->const_data_ptr<scalar_t>(),
+              args.lda,
+              args.matb->const_data_ptr<scalar_t>(),
+              args.ldb,
+              self.const_data_ptr<scalar_t>(),
+              args.result->data_ptr<scalar_t>(),
+              args.result_ld,
+              activation_epilogue
+          );
+      }});
+    }
+    if (!okay) {
+      // lt path failed; recurse but disable lt path
+      return addmm_out_cuda_impl(result, self, mat1, mat2, beta, alpha, activation, true);
+    }
+#endif
+  } else
+  {
     if (is_float_output_with_half_input) {
       AT_DISPATCH_REDUCED_FLOATING_TYPES(
         scalar_type,
         "addmm_cuda",
         [&] {
-          launchGemmCublas<scalar_t, float>(args, alpha, beta);
-        }
-      );
+          using opmath_t = at::opmath_type<scalar_t>;
+          opmath_t alpha_val = alpha.to<opmath_t>();
+          opmath_t beta_val = beta.to<opmath_t>();
+          const scalar_t* mat1_ptr = args.mata->const_data_ptr<scalar_t>();
+          const scalar_t* mat2_ptr = args.matb->const_data_ptr<scalar_t>();
+
+          float* result_ptr = args.result->mutable_data_ptr<float>();
+          at::cuda::blas::gemm<scalar_t, float>(
+              args.transa,
+              args.transb,
+              args.m,
+              args.n,
+              args.k,
+              alpha_val,
+              mat1_ptr,
+              args.lda,
+              mat2_ptr,
+              args.ldb,
+              beta_val,
+              result_ptr,
+              args.result_ld);
+        });
     } else {
       AT_DISPATCH_FLOATING_AND_COMPLEX_TYPES_AND2(
         at::ScalarType::Half,
@@ -622,12 +614,28 @@ Tensor& addmm_out_cuda_impl(Tensor& result, const Tensor& self, const Tensor& ma
         scalar_type,
         "addmm_cuda",
         [&] {
-          launchGemmCublas<scalar_t>(args, alpha, beta);
-        }
-      );
+          using opmath_t = at::opmath_type<scalar_t>;
+          opmath_t alpha_val = alpha.to<opmath_t>();
+          opmath_t beta_val = beta.to<opmath_t>();
+          const scalar_t* mat1_ptr = args.mata->const_data_ptr<scalar_t>();
+          const scalar_t* mat2_ptr = args.matb->const_data_ptr<scalar_t>();
+          scalar_t* result_ptr = args.result->mutable_data_ptr<scalar_t>();
+          at::cuda::blas::gemm<scalar_t>(
+              args.transa,
+              args.transb,
+              args.m,
+              args.n,
+              args.k,
+              alpha_val,
+              mat1_ptr,
+              args.lda,
+              mat2_ptr,
+              args.ldb,
+              beta_val,
+              result_ptr,
+              args.result_ld);
+        });
     }
-
-    // Apply epilogue
     switch (activation) {
       case Activation::RELU:
         // NOLINTNEXTLINE(cppcoreguidelines-pro-type-const-cast)
@@ -639,14 +647,14 @@ Tensor& addmm_out_cuda_impl(Tensor& result, const Tensor& self, const Tensor& ma
         break;
       default: break;
     }
-  } // end GEMM path
+  }
 
 // Preprocessor gate here needs to match the inverse of the check
 // gating activation_to_gemm_and_blas_arg above; here we are manually
 // performing a post-GELU because we weren't able to use the GELU
 // epilogue above.
 #if !defined(CUDA_VERSION) && !defined(USE_ROCM)
-  if (!disable_addmm_cuda_lt && activation == Activation::GELU) {
+  if (useLtInterface && activation == Activation::GELU) {
     at::gelu_(const_cast<Tensor&>(*args.result), "tanh");
   }
 #endif
@@ -2314,23 +2322,12 @@ _scaled_nvfp4_nvfp4(
           const Tensor& scale_b, const SwizzleType swizzle_b,
           const std::optional<Tensor>& bias,
           const c10::ScalarType out_dtype,
-          Tensor& out,
-          const std::optional<Tensor>& global_scale_a = std::nullopt,
-          const std::optional<Tensor>& global_scale_b = std::nullopt) {
+          const bool single_scale,
+          Tensor& out) {
 #ifdef USE_ROCM
   TORCH_CHECK_NOT_IMPLEMENTED(false, "NVFP4 scaling not supported on ROCM");
 #endif
-  std::optional<Tensor> alpha = std::nullopt;
-  // Note: "Or" here means that if only one scale is passed, we check for the other. Otherwise,
-  //       if this is "And" we would silently do nothing in the case where one global scale is
-  //       passed and not the other.
-  if (global_scale_a.has_value() || global_scale_b.has_value()) {
-    TORCH_CHECK_VALUE(global_scale_a.has_value(),
-        "For two-level-scaled NVFP4, global_scale_a must have a value");
-    TORCH_CHECK_VALUE(global_scale_b.has_value(),
-        "For two-level-scaled NVFP4, global_scale_b must have a value");
-    alpha = global_scale_a.value().mul(global_scale_b.value());
-  }
+  TORCH_CHECK_VALUE(single_scale, "Only single-scaled NVFP4 currently supported");
   // Restrictions:
   // A, B are FP4, scales are e8m0, A: shape K//32, B: K, N//32
   // Scales must be swizzled
@@ -2352,7 +2349,7 @@ _scaled_nvfp4_nvfp4(
 
   auto scaling_choice_a = ScalingType::BlockWise1x16;
   auto scaling_choice_b = ScalingType::BlockWise1x16;
-  return _scaled_gemm(mat_a, mat_b, scale_a, scale_b, scaling_choice_a, scaling_choice_b, bias, false /* use_fast_accum */, out, alpha);
+  return _scaled_gemm(mat_a, mat_b, scale_a, scale_b, scaling_choice_a, scaling_choice_b, bias, false /* use_fast_accum */, out);
 }
 
 
@@ -2558,10 +2555,9 @@ _scaled_mm_cuda_v2_out(
   } else if (gemm_impl == ScaledGemmImplementation::MXFP8_MXFP8) {
     return _scaled_mxfp8_mxfp8(mat_a, mat_b, scale_a[0], swizzle_a_enum[0], scale_b[0], swizzle_b_enum[0], bias, out_dtype_, out);
   } else if (gemm_impl == ScaledGemmImplementation::NVFP4_NVFP4) {
-    return _scaled_nvfp4_nvfp4(mat_a, mat_b, scale_a[0], swizzle_a_enum[0], scale_b[0], swizzle_b_enum[0], bias, out_dtype_, out,
-                               scale_a[1], scale_b[1]);
+    TORCH_CHECK_NOT_IMPLEMENTED(false, "Only single-scale NVFP4 currently supported");
   } else if (gemm_impl == ScaledGemmImplementation::NVFP4_NVFP4_SINGLE_SCALE) {
-    return _scaled_nvfp4_nvfp4(mat_a, mat_b, scale_a[0], swizzle_a_enum[0], scale_b[0], swizzle_b_enum[0], bias, out_dtype_, out);
+    return _scaled_nvfp4_nvfp4(mat_a, mat_b, scale_a[0], swizzle_a_enum[0], scale_b[0], swizzle_b_enum[0], bias, out_dtype_, true /* single_scale */, out);
   } else if (gemm_impl == ScaledGemmImplementation::MXFP4_MXFP4) {
     return _scaled_mxfp4_mxfp4(mat_a, mat_b, scale_a[0], swizzle_a_enum[0], scale_b[0], swizzle_b_enum[0], bias, out_dtype_, out);
   } else {

aten/src/ATen/native/cuda/Embedding.cu

@@ -15,7 +15,9 @@
 #include <ATen/native/cuda/block_reduce.cuh>
 #include <ATen/native/cuda/thread_constants.h>
 
+#if CUB_SUPPORTS_SCAN_BY_KEY()
 #include <thrust/iterator/reverse_iterator.h>
+#endif
 
 #ifndef AT_PER_OPERATOR_HEADERS
 #include <ATen/Functions.h>
@@ -238,6 +240,10 @@ __global__ void renorm_kernel(
 
 } // anonymous namespace
 
+#if !CUB_SUPPORTS_SCAN_BY_KEY()
+template<typename index_t>
+void embedding_dense_backward_cuda_scan(Tensor &sorted_indices, Tensor &count);
+#endif
 
 Tensor embedding_dense_backward_cuda(const Tensor & grad_, const Tensor & indices_,
                                int64_t num_weights, int64_t padding_idx,
@@ -300,6 +306,7 @@ Tensor embedding_dense_backward_cuda(const Tensor & grad_, const Tensor & indice
 
   if (scale_grad_by_freq) {
     count = at::empty_like(indices, LEGACY_CONTIGUOUS_MEMORY_FORMAT);
+#if CUB_SUPPORTS_SCAN_BY_KEY()
     AT_DISPATCH_INDEX_TYPES(indices.scalar_type(), "embedding_dense_backward_cuda", [&] () {
       cudaStream_t stream = at::cuda::getCurrentCUDAStream();
 
@@ -326,6 +333,11 @@ Tensor embedding_dense_backward_cuda(const Tensor & grad_, const Tensor & indice
         num_indices
       );
     });
+#else
+    AT_DISPATCH_INDEX_TYPES(indices.scalar_type(), "embedding_dense_backward_cuda", [&] () {
+      embedding_dense_backward_cuda_scan<index_t>(sorted_indices, count);
+    });
+#endif
   }
 
   return embedding_backward_cuda_kernel(grad, orig_indices,

aten/src/ATen/native/cuda/EmbeddingBackwardKernel.cu

@@ -10,7 +10,9 @@
 
 #include <c10/macros/Macros.h>
 
+#if CUB_SUPPORTS_UNIQUE_BY_KEY()
 #include <thrust/iterator/counting_iterator.h>
+#endif
 
 #ifndef AT_PER_OPERATOR_HEADERS
 #include <ATen/Functions.h>
@@ -194,9 +196,18 @@ __global__ void compute_num_of_partial_segments(const index_t *partials_per_segm
             partials_per_segment_offset[num_of_segments-1];
 }
 
+#if !CUB_SUPPORTS_UNIQUE_BY_KEY()
+__global__ void write_num_of_segments_for_legacy_thrust_path(int64_t *num_of_segments_ptr, int64_t num_of_segments) {
+  *num_of_segments_ptr = num_of_segments;
+}
+#endif
 
 } // anon namespace
 
+#if !CUB_SUPPORTS_UNIQUE_BY_KEY()
+template<typename index_t>
+int64_t embedding_backward_cuda_kernel_unique_by_key(const Tensor &sorted_indices, Tensor &segment_offsets);
+#endif
 
 Tensor embedding_backward_cuda_kernel(
         const Tensor &grad,
@@ -223,12 +234,20 @@ Tensor embedding_backward_cuda_kernel(
   auto segment_offsets = at::empty({numel}, orig_indices.options());
   auto num_of_segments_tensor = at::empty({}, grad.options().dtype(kLong));
   int64_t *num_of_segments_ptr = num_of_segments_tensor.mutable_data_ptr<int64_t>();
+#if !CUB_SUPPORTS_UNIQUE_BY_KEY()
+  AT_DISPATCH_INDEX_TYPES(orig_indices.scalar_type(), "embedding_backward_cuda_kernel", [&] () {
+    int64_t num_of_segments = embedding_backward_cuda_kernel_unique_by_key<index_t>(sorted_indices, segment_offsets);
+    write_num_of_segments_for_legacy_thrust_path<<<1, 1, 0, c10::cuda::getCurrentCUDAStream()>>>(num_of_segments_ptr, num_of_segments);
+    C10_CUDA_KERNEL_LAUNCH_CHECK();
+  });
+#else
   AT_DISPATCH_INDEX_TYPES(orig_indices.scalar_type(), "embedding_backward_cuda_kernel", [&] () {
     cuda::cub::unique_by_key(
       sorted_indices.const_data_ptr<index_t>(), thrust::make_counting_iterator(0),
       segment_offsets.mutable_data_ptr<index_t>(),
       num_of_segments_ptr, sorted_indices.numel());
   });
+#endif
 
   int64_t max_segments = std::min<int64_t>(numel, num_weights);
 

aten/src/ATen/native/cuda/EmbeddingBag.cu

@@ -31,10 +31,16 @@
 
 #include <c10/macros/Macros.h>
 
+#if CUB_SUPPORTS_SCAN_BY_KEY()
 #include <thrust/iterator/reverse_iterator.h>
+#endif
 
 namespace at::native {
 
+#if !CUB_SUPPORTS_SCAN_BY_KEY()
+template<typename index_t>
+void embedding_dense_backward_cuda_scan(Tensor &sorted_indices, Tensor &count);
+#endif
 
 namespace {
 
@@ -193,6 +199,7 @@ Tensor embedding_bag_backward_cuda_sum_avg(
 
   if (scale_grad_by_freq) {
     count = at::empty_like(indices, LEGACY_CONTIGUOUS_MEMORY_FORMAT);
+#if CUB_SUPPORTS_SCAN_BY_KEY()
     AT_DISPATCH_INDEX_TYPES(indices.scalar_type(), "embedding_bag_backward_cuda_sum_avg", [&] () {
       cudaStream_t stream = at::cuda::getCurrentCUDAStream();
 
@@ -219,6 +226,11 @@ Tensor embedding_bag_backward_cuda_sum_avg(
         num_indices
       );
     });
+#else
+    AT_DISPATCH_INDEX_TYPES(indices.scalar_type(), "embedding_bag_backward_cuda_sum_avg", [&] () {
+      embedding_dense_backward_cuda_scan<index_t>(sorted_indices, count);
+    });
+#endif
   }
   return embedding_backward_cuda_kernel(grad, orig_indices, sorted_indices,
       count, num_weights, padding_idx, mode == EmbeddingBagMode::MEAN, offset2bag,

aten/src/ATen/native/cuda/LegacyThrustHelpers.cu

@@ -0,0 +1,90 @@
+#define TORCH_ASSERT_ONLY_METHOD_OPERATORS
+#include <ATen/core/Tensor.h>
+#include <ATen/native/cuda/SortingCommon.cuh>
+#include <ATen/cuda/cub_definitions.cuh>
+
+#ifndef AT_PER_OPERATOR_HEADERS
+#include <ATen/Functions.h>
+#else
+#include <ATen/ops/empty_like.h>
+#endif
+
+#include <ATen/cuda/ThrustAllocator.h>
+#include <thrust/device_ptr.h>
+#include <thrust/execution_policy.h>
+#include <thrust/sort.h>
+#include <thrust/unique.h>
+#include <thrust/device_ptr.h>
+#include <thrust/iterator/constant_iterator.h>
+
+namespace at::native {
+
+#if !CUB_SUPPORTS_SCAN_BY_KEY()
+
+template<typename index_t>
+void embedding_dense_backward_cuda_scan(Tensor &sorted_indices, Tensor &count) {
+  cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+  at::cuda::ThrustAllocator allocator;
+  auto policy = thrust::cuda::par(allocator).on(stream);
+
+  auto num_indices = count.numel();
+
+  // Compute an increasing sequence per unique item in sortedIndices:
+  // sorted: 2 5 5 5 7 7 8 9 9
+  //  count: 1 1 2 3 1 2 1 1 2
+  auto sorted_data = thrust::device_ptr<const index_t>(sorted_indices.const_data_ptr<index_t>());
+  auto count_data = thrust::device_ptr<index_t>(count.mutable_data_ptr<index_t>());
+  thrust::inclusive_scan_by_key(
+    policy,
+    sorted_data,
+    sorted_data + num_indices,
+    thrust::make_constant_iterator(1),
+    count_data
+  );
+
+  // Take the maximum of each count per unique key in reverse:
+  // sorted: 2 5 5 5 7 7 8 9 9
+  //  count: 1 3 3 3 2 2 1 2 2
+  thrust::inclusive_scan_by_key(
+    policy,
+    thrust::make_reverse_iterator(sorted_data + num_indices),
+    thrust::make_reverse_iterator(sorted_data),
+    thrust::make_reverse_iterator(count_data + num_indices),
+    thrust::make_reverse_iterator(count_data + num_indices),
+    thrust::equal_to<index_t>(),
+    thrust::maximum<index_t>()
+  );
+}
+
+template
+void embedding_dense_backward_cuda_scan<int>(Tensor &sorted_indices, Tensor &count);
+template
+void embedding_dense_backward_cuda_scan<int64_t>(Tensor &sorted_indices, Tensor &count);
+
+#endif
+
+template<typename index_t>
+int64_t embedding_backward_cuda_kernel_unique_by_key(const Tensor &sorted_indices, Tensor &segment_offsets) {
+  auto stream = at::cuda::getCurrentCUDAStream();
+  at::cuda::ThrustAllocator allocator;
+  auto policy = thrust::cuda::par(allocator).on(stream);
+  const ptrdiff_t numel = sorted_indices.numel();
+  auto sorted_indices_dev = thrust::device_ptr<const index_t>(sorted_indices.const_data_ptr<index_t>());
+  auto dummy = at::empty_like(sorted_indices, LEGACY_CONTIGUOUS_MEMORY_FORMAT);
+  auto dummy_dev = thrust::device_ptr<index_t>(dummy.mutable_data_ptr<index_t>());
+  auto ends = thrust::unique_by_key_copy(
+          policy,
+          sorted_indices_dev,
+          sorted_indices_dev + numel,
+          thrust::make_counting_iterator(0),
+          dummy_dev,
+          thrust::device_ptr<index_t>(segment_offsets.mutable_data_ptr<index_t>()));
+  return thrust::get<0>(ends) - dummy_dev;
+}
+
+template
+int64_t embedding_backward_cuda_kernel_unique_by_key<int>(const Tensor &sorted_indices, Tensor &segment_offsets);
+template
+int64_t embedding_backward_cuda_kernel_unique_by_key<int64_t>(const Tensor &sorted_indices, Tensor &segment_offsets);
+
+} // namespace at::native

aten/src/ATen/native/cuda/Loops.cuh

@@ -1,17 +1,18 @@
 #pragma once
 
-#include <ATen/OpMathType.h>
-#include <ATen/cuda/detail/OffsetCalculator.cuh>
 #include <ATen/detail/FunctionTraits.h>
 #include <ATen/native/TensorIterator.h>
 #include <ATen/native/TensorIteratorDynamicCasting.h>
+#include <ATen/cuda/detail/OffsetCalculator.cuh>
+#include <ATen/OpMathType.h>
 #include <ATen/native/cuda/thread_constants.h>
+
+#include <thrust/tuple.h>
+
 #include <ATen/native/cuda/MemoryAccess.cuh>
 
 #include <tuple>
 
-
-
 namespace at::native {
 
 template<int N>
@@ -61,11 +62,7 @@ __device__ inline void elementwise_kernel_helper(func_t f, policy_t policy) {
   #pragma unroll
   for (int i = 0; i < elems_per_thread; i++) {
     if (policy.check_inbounds(i)) {
-#if defined(__HIP__)
       results[i] = c10::guts::apply(f, args[i]);
-#else
-      results[i] = std::apply(f, args[i]);
-#endif
     }
   }
 

aten/src/ATen/native/cuda/NLLLoss2d.cu

@@ -146,7 +146,6 @@ __global__ void nll_loss2d_backward_no_reduce_kernel(
   int64_t batch_size = target.size(0);
   int64_t H = target.size(1);
   int64_t W = target.size(2);
-  int64_t n_classes = grad_input.size(1);
 
   CUDA_KERNEL_LOOP(index, n_threads) {
     const int64_t b = index % batch_size;
@@ -157,7 +156,6 @@ __global__ void nll_loss2d_backward_no_reduce_kernel(
     if (cur_target == ignore_index) {
       continue;
     }
-    CUDA_KERNEL_ASSERT(cur_target >= 0 && cur_target < n_classes);
     scalar_t value = -(weight != nullptr ? weight[cur_target] : static_cast<scalar_t>(1));
     grad_input[b][cur_target][h][w] = value * grad_output[b][h][w];
   }

aten/src/ATen/native/cuda/Normalization.cuh

@@ -23,7 +23,7 @@ namespace at::native {
 
 // The maximum number of threads in a block
 #if defined(USE_ROCM)
-constexpr int MAX_BLOCK_SIZE = 1024;
+constexpr int MAX_BLOCK_SIZE = 256;
 #else
 constexpr int MAX_BLOCK_SIZE = 512;
 #endif
@@ -33,7 +33,7 @@ constexpr unsigned MAX_GRID_SIZE = 65535u;
 // Number of threads in a block given an input size up to MAX_BLOCK_SIZE
 static int getNumThreads(int nElem) {
 #if defined(USE_ROCM)
-  int threadSizes[5] = { 64, 128, 256, 512, MAX_BLOCK_SIZE };
+  int threadSizes[5] = { 16, 32, 64, 128, MAX_BLOCK_SIZE };
 #else
   int threadSizes[5] = { 32, 64, 128, 256, MAX_BLOCK_SIZE };
 #endif
@@ -115,23 +115,9 @@ __device__ scalar_t reduce(Op op, PTA tensor, int plane) {
   // first the reductions each thread does separately
   scalar_t sum = static_cast<scalar_t>(0);
   for (int batch = threadIdx.y; batch < tensor.size(0); batch += blockDim.y) {
-#if defined(USE_ROCM)
-    constexpr int UNRL = 4; // load deserilize factor
-    scalar_t tmp[UNRL];
-    for (int x = threadIdx.x; x < tensor.size(2); x += blockDim.x*UNRL) {
-#pragma unroll
-      for (int u = 0; u < UNRL; u++)
-        tmp[u] = op(batch, plane, min((int)tensor.size(2)-1, (int)(x+u*blockDim.x)));
-#pragma unroll
-      for (int u = 0; u < UNRL; u++)
-        if (x+u*blockDim.x < tensor.size(2))
-          sum += tmp[u];
-    }
-#else
     for (int x = threadIdx.x; x < tensor.size(2); x += blockDim.x) {
       sum += op(batch, plane, x);
     }
-#endif
   }
   __shared__ scalar_t shared[C10_WARP_SIZE];
   SumReduceOp<scalar_t> reduce_op;
@@ -306,22 +292,6 @@ __global__ void batch_norm_collect_statistics_kernel(
   stat_accscalar_t var_n = 0;
   int n = 0;
   for (int batch = threadIdx.y; batch < input.size(0); batch += blockDim.y) {
-#if defined(USE_ROCM)
-    constexpr int UNRL = 4;
-    stat_accscalar_t v_[UNRL];
-    for (int x = threadIdx.x; x < input.size(2); x += blockDim.x*UNRL) {
-      for (int u = 0; u < UNRL; u++)
-        v_[u] = input[batch][plane][min(x+u*blockDim.x, input.size(2)-1)];
-      for (int u = 0; u < UNRL; u++) {
-        if (x+u*blockDim.x < input.size(2)) {
-          stat_accscalar_t d1 = v_[u] - avg;
-          n++;
-          avg += d1 / n;
-          var_n += d1 * (v_[u] - avg);
-        }
-      }
-    }
-#else
     for (int x = threadIdx.x; x < input.size(2); x += blockDim.x) {
       stat_accscalar_t v = input[batch][plane][x];
       stat_accscalar_t d1 = v - avg;
@@ -329,7 +299,6 @@ __global__ void batch_norm_collect_statistics_kernel(
       avg += d1 / n;
       var_n += d1 * (v - avg);
     }
-#endif
   }
 
   // first warpSum to get one value per thread to

aten/src/ATen/native/cuda/Reduce.cuh

@@ -413,12 +413,14 @@ struct ReduceOp {
       value = thread_reduce<output_vec_size>(input_slice);
     }
 
-    if (config.should_block_x_reduce()) {
-      value = block_x_reduce<output_vec_size>(value, shared_memory);
-    }
     if (config.should_block_y_reduce()) {
       value = block_y_reduce<output_vec_size>(value, shared_memory);
     }
+    __syncthreads();
+    if (config.should_block_x_reduce()) {
+      value = block_x_reduce<output_vec_size>(value, shared_memory);
+    }
+
     using out_ptr_vec_t = std::array<out_scalar_t*, output_vec_size>;
     using offset_vec_t = std::array<index_t, output_vec_size>;
     offset_vec_t base_offsets;
@@ -655,8 +657,8 @@ struct ReduceOp {
     __syncthreads();
     // Intra-warp reduction, fix CUDA to have offset decreasing for better numerics
     // matching Triton, etc.
-    // TODO(PaulZhang12): AMD and internal
-    #if defined(USE_ROCM) || defined(FBCODE_CAFFE2)
+    // todo for AMD
+    #ifdef USE_ROCM
     for (int offset = 1; offset < dim_x; offset <<= 1) {
     #else
     for (int offset = dim_x >> 1; offset > 0; offset >>= 1) {

aten/src/ATen/native/cuda/ReflectionPad.cu

@@ -92,16 +92,6 @@ inline thrust::pair<int64_t, int64_t>  get_index_mapping2d(
     output_offset + output_y * output_dim_x + output_x);
 }
 
-__device__ __forceinline__ int64_t reflect_index(int64_t x, int64_t len) {
-  const int64_t two = (len - 1) * 2;
-  if (two <= 0) {
-    return 0;
-  }
-  int64_t m = x % two;
-  if (m < 0) m += two;
-  return (m < len) ? m : (two - m);
-}
-
 template<typename scalar_t>
 __global__ void reflection_pad1d_out_kernel(
     const scalar_t * input, scalar_t * output,
@@ -116,28 +106,6 @@ __global__ void reflection_pad1d_out_kernel(
   }
 }
 
-template <typename scalar_t>
-__global__ void reflection_pad1d_flat(
-    const scalar_t* __restrict__ input,
-    scalar_t* __restrict__ output,
-    int64_t input_w, int64_t pad_l, int64_t pad_r,
-    int64_t out_w, int64_t plane_count) {
-
-  const int64_t bx = blockDim.x;
-  const int64_t tx = threadIdx.x;
-
-  const int64_t total = plane_count * out_w;
-  const int64_t grid_stride = static_cast<int64_t>(bx) * gridDim.x;
-  int64_t linear = static_cast<int64_t>(blockIdx.x) * bx + tx;
-
-  for (; linear < total; linear += grid_stride) {
-    const int64_t plane = linear / out_w;
-    const int64_t x = linear - plane * out_w;
-    const int64_t j = reflect_index(x - pad_l, input_w);
-    output[plane * out_w + x] = input[plane * input_w + j];
-  }
-}
-
 template <typename scalar_t>
 __global__ void reflection_pad1d_backward_out_kernel(
     scalar_t * grad_input, const scalar_t * grad_output,
@@ -742,44 +710,25 @@ TORCH_IMPL_FUNC(reflection_pad1d_out_cuda)
   int64_t input_w = input_.size(dim_w);
   int64_t output_w = input_w + pad_l + pad_r;
 
+  dim3 block_size(output_w > 256 ? 256 : output_w);
+  dim3 grid_size((int)::ceil(output_w / 256.0), nplane, nbatch);
 
   Tensor input = input_.contiguous();
 
-  const int block_x = static_cast<int>(std::min<int64_t>(256, std::max<int64_t>(1, output_w)));
-  const cudaDeviceProp* prop = at::cuda::getCurrentDeviceProperties();
-  const int max_x = prop->maxGridSize[0];
-  const int max_y = prop->maxGridSize[1];
-  const int max_z = prop->maxGridSize[2];
-
-  AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND2(kHalf, kBFloat16, input.scalar_type(), "reflection_pad1d_out", [&] {
-    auto stream = at::cuda::getCurrentCUDAStream();
-
-    const int64_t gx = at::ceil_div(output_w, static_cast<int64_t>(block_x));
-
-    const bool fits3d = (nplane <= max_y) && (nbatch <= max_z) && (gx <= max_x);
-
-    if (fits3d) {
-      dim3 block(block_x, 1, 1);
-      dim3 grid(gx, static_cast<unsigned>(nplane), static_cast<unsigned>(nbatch));
-      reflection_pad1d_out_kernel<scalar_t><<<grid, block, 0, stream>>>(
-          input.const_data_ptr<scalar_t>(),
-          output.mutable_data_ptr<scalar_t>(),
-          input_w, pad_l, pad_r);
-    } else {
-      dim3 block(block_x, 1, 1);
-      const int64_t plane_count = nplane * nbatch;
-      const int64_t total_blocks = at::ceil_div(plane_count * output_w, static_cast<int64_t>(block_x));
-      const int grid_x = static_cast<int>(std::min<int64_t>(max_x, std::max<int64_t>(1, total_blocks)));
-      dim3 grid(grid_x, 1, 1);
-
-      reflection_pad1d_flat<scalar_t><<<grid, block, 0, stream>>>(
-          input.const_data_ptr<scalar_t>(),
-          output.mutable_data_ptr<scalar_t>(),
-          input_w, pad_l, pad_r, output_w, plane_count);
-    }
-
-    C10_CUDA_KERNEL_LAUNCH_CHECK();
-  });
+  AT_DISPATCH_ALL_TYPES_AND_COMPLEX_AND2(
+      kHalf, kBFloat16, input.scalar_type(), "reflection_pad1d_out_template", [&] {
+        reflection_pad1d_out_kernel<<<
+            grid_size,
+            block_size,
+            0,
+            at::cuda::getCurrentCUDAStream()>>>(
+            input.const_data_ptr<scalar_t>(),
+            output.mutable_data_ptr<scalar_t>(),
+            input_w,
+            pad_l,
+            pad_r);
+        C10_CUDA_KERNEL_LAUNCH_CHECK();
+      });
 }
 
 TORCH_IMPL_FUNC(reflection_pad1d_backward_out_cuda)(const Tensor& grad_output_,

aten/src/ATen/native/cuda/TensorTopK.cpp

@@ -19,6 +19,7 @@
 
 namespace at::native {
 
+// TODO: remove this when CUDA <11.6 is no longer supported
 void topk_out_with_sort(
   const Tensor& self,
   int64_t k, int64_t dim, bool largest,
@@ -30,12 +31,21 @@ void topk_out_with_sort(
   indices.copy_(sorted_indices.narrow(dim, 0, k));
 }
 
+// TODO: remove this when CUDA <11.6 is no longer supported
+bool disable_sort_for_topk();
 bool should_use_sort(const Tensor& self, int64_t dim) {
 #if defined(USE_ROCM)
   if (self.dtype() == kBool) return false; // Bool sort not supported in ROCm: https://github.com/pytorch/pytorch/issues/139972
   return (self.numel() >= 10000 && self.numel() == self.size(dim)); // based on the experiments in https://github.com/pytorch/pytorch/pull/146387
 #else
-  return false;
+  if (disable_sort_for_topk()) return false;
+  // This heuristics is based on the experiment in https://github.com/pytorch/pytorch/pull/68632
+  if (self.dim() == 0) return false;
+  if (self.dtype() == kBool) return false; // Bool is not support by topk
+  int64_t slice_size = self.size(dim);
+  if (slice_size == 0) return false;
+  int64_t num_slices = self.numel() / slice_size;
+  return num_slices <= 10 && slice_size >= 100000;
 #endif
 }
 

aten/src/ATen/native/cuda/TensorTopK.cu

@@ -21,6 +21,11 @@ using namespace at::native;
 
 namespace at::native {
 
+// TODO: remove this when CUDA <11.6 is no longer supported
+bool disable_sort_for_topk() {
+  return CUB_SUPPORTS_SCAN_BY_KEY();
+}
+
 namespace sbtopk { // single_block_topk
 
 template <typename T>
@@ -413,6 +418,10 @@ __global__ void computeBlockwiseWithinKCounts(
   }
   __syncthreads();
 
+#if !CUB_SUPPORTS_SCAN_BY_KEY()
+  return;
+#endif
+
   Bitwise desired_digit = at::cuda::Bitfield<Bitwise>::getBitfield(desired, current_bit, RADIX_BITS);
 
   // if largest, then only threads that has tidx > desired_digit are active
@@ -468,6 +477,7 @@ __global__ void computeBlockwiseWithinKCounts(
   }
 }
 
+#if CUB_SUPPORTS_SCAN_BY_KEY()
 // Assumption: slice_size can not be larger than UINT32_MAX
 template <typename Bitwise>
 __global__ void computeBlockwiseKthCounts(
@@ -599,6 +609,7 @@ __global__ void gatherTopK(at::cuda::detail::TensorInfo<const T, IndexType> inpu
     }
   }
 }
+#endif
 
 int get_items_per_thread(uint64_t num_slices, uint64_t slice_size) {
   // occupancy of this kernel is limited by registers per threads
@@ -676,12 +687,16 @@ void launch(
   uint32_t* digit_cum_sum = reinterpret_cast<uint32_t*>(digit_cum_sum_buffer.get());
   AT_CUDA_CHECK(cudaMemsetAsync(digit_cum_sum, 0, numInputSlices * RADIX_DIGITS * sizeof(uint32_t), stream));
 
+#if CUB_SUPPORTS_SCAN_BY_KEY()
   auto withinKCounts_buffer = allocator.allocate(num_blocks * sizeof(uint32_t));
   uint32_t* withinKCounts = reinterpret_cast<uint32_t*>(withinKCounts_buffer.get());
   AT_CUDA_CHECK(cudaMemsetAsync(withinKCounts, 0, num_blocks * sizeof(uint32_t), stream));
 
   auto kthCounts_buffer = allocator.allocate(num_blocks * sizeof(uint32_t));
   uint32_t* kthCounts = reinterpret_cast<uint32_t*>(kthCounts_buffer.get());
+#else
+  uint32_t* withinKCounts = nullptr;
+#endif
 
   Bitwise desiredMask = 0;
   dim3 grid;
@@ -728,6 +743,7 @@ void launch(
   }
   desired = desired_in;
 
+#if CUB_SUPPORTS_SCAN_BY_KEY()
   computeBlockwiseKthCounts<Bitwise><<<std::min(((int64_t)numInputSlices + 255) / 256, (int64_t)1073741824), 256, 0, stream>>>(
     desired, counts, num_blocks, blocks_per_slice, kthCounts);
   C10_CUDA_KERNEL_LAUNCH_CHECK();
@@ -743,6 +759,28 @@ void launch(
     topK, topKWithinSliceStride, indices, indicesWithinSliceStride, items_per_thread,
     blocks_per_slice, kthValues, withinKCounts, kthCounts, num_blocks);
   C10_CUDA_KERNEL_LAUNCH_CHECK();
+#else
+  // Find topk values based on kth values
+  {
+    dim3 grid;
+    TORCH_INTERNAL_ASSERT(getGridFromTiles(numInputSlices, grid), "Too many slices for topk");
+    int warp_size = at::cuda::warp_size();
+    dim3 block(std::min(at::ceil_div((int64_t)inputSliceSize, (int64_t)warp_size) * (int64_t)warp_size, (int64_t)1024));
+    sbtopk::gatherTopK<T, IndexType, Dim, /* WithKthValues= */true><<<grid, block, 0, stream>>>(
+        input,
+        inputSliceSize,
+        outputSliceSize,
+        largest,
+        numInputSlices,
+        inputWithinSliceStride,
+        topK,
+        topKWithinSliceStride,
+        indices,
+        indicesWithinSliceStride,
+        kthValues);
+    C10_CUDA_KERNEL_LAUNCH_CHECK();
+  }
+#endif
 }
 
 } // namespace mbtopk
@@ -750,6 +788,7 @@ void launch(
 bool should_use_multiblock(int64_t num_slices, int64_t slice_size) {
   if (num_slices > std::numeric_limits<uint32_t>::max() ||
       slice_size > std::numeric_limits<uint32_t>::max()) return false;
+#if CUB_SUPPORTS_SCAN_BY_KEY()
   // This heuristics is based on the experiment in https://github.com/pytorch/pytorch/pull/74267
   return (num_slices <= 20 && slice_size >= 20000) ||
       (num_slices > 20 && num_slices <= 40 && slice_size >= 10000) ||
@@ -758,6 +797,12 @@ bool should_use_multiblock(int64_t num_slices, int64_t slice_size) {
       (num_slices >= 200 && num_slices < 800 && slice_size >= 3000) ||
       (num_slices >= 800 && num_slices <= 4000 && slice_size >= 800) ||
       (num_slices > 4000 && slice_size >= 400);
+#else
+  // This heuristics is based on the experiment in https://github.com/pytorch/pytorch/pull/71081
+  return (num_slices <= 400 && slice_size >= 5000) ||
+      (num_slices > 400 && num_slices < 4000 && slice_size >= 1000) ||
+      (num_slices >= 4000 && slice_size >= 300);
+#endif
 }
 
 void launch_gather_topk_kernel(

aten/src/ATen/native/cuda/TriangularOps.cu

@@ -44,7 +44,7 @@ __global__ void triu_tril_kernel(
     const int64_t k,
     const int64_t N_padded,
     const IndexType last_dim_padded) {
-  int64_t linear_idx = (((int64_t)blockIdx.x) * blockDim.x + threadIdx.x) * elements_per_thread;
+  int64_t linear_idx = (blockIdx.x * blockDim.x + threadIdx.x) * elements_per_thread;
   if (linear_idx >= N_padded) {
     return;
   }

aten/src/ATen/native/cuda/UpSampleBilinear2d.cu

@@ -127,6 +127,29 @@ __global__ void upsample_bilinear2d_nhwc_out_frame(
   }
 }
 
+#ifdef USE_ROCM
+// Helper function to compute output pixel range that can contribute to input pixel
+template <typename accscalar_t>
+__device__ __forceinline__ void compute_output_range(
+    int input_pos,
+    accscalar_t scale,
+    int output_size,
+    bool align_corners,
+    int& min_output,
+    int& max_output) {
+  accscalar_t lo, hi;
+  if (align_corners) {
+      lo = static_cast<accscalar_t>(input_pos - 1) / scale;
+      hi = static_cast<accscalar_t>(input_pos + 1) / scale;
+  } else {
+      lo = (input_pos - static_cast<accscalar_t>(0.5)) / scale - static_cast<accscalar_t>(0.5);
+      hi = (input_pos + static_cast<accscalar_t>(1.5)) / scale - static_cast<accscalar_t>(0.5);
+  }
+  min_output = max(0, static_cast<int>(ceil(lo)));
+  max_output = min(output_size - 1, static_cast<int>(floor(hi)));
+}
+#endif
+
 // Backward (adjoint) operation 1 <- 2 (accumulates)
 template <typename scalar_t, typename accscalar_t>
 C10_LAUNCH_BOUNDS_1(1024)
@@ -141,8 +164,74 @@ __global__ void upsample_bilinear2d_backward_out_frame(
     const bool align_corners,
     scalar_t* __restrict__ idata,
     const scalar_t* __restrict__ odata) {
-  const size_t o_numel = nc * width2 * height2;
+  // In C++, integer multiplication, like in standard arithmetic, is generally commutative.
   const size_t i_numel = nc * width1 * height1;
+#ifdef USE_ROCM
+  for (size_t index = blockDim.x * blockIdx.x + threadIdx.x; index < i_numel;
+       index += blockDim.x * gridDim.x) {
+    // Decode input pixel coordinates
+    size_t index_temp = index;
+    const int w1 = index_temp % width1;
+    index_temp /= width1;
+    const int h1 = index_temp % height1;
+    const size_t nc_idx = index_temp / height1;
+
+    accscalar_t grad_sum = 0;
+
+    // Find range of output pixels that could interpolate from this input pixel
+    int h2_min, h2_max, w2_min, w2_max;
+    compute_output_range<accscalar_t>(h1, rheight, height2, align_corners, h2_min, h2_max);
+    compute_output_range<accscalar_t>(w1, rwidth, width2, align_corners, w2_min, w2_max);
+
+    // Iterate over potential output pixels
+    for (int h2 = h2_min; h2 <= h2_max; h2++) {
+      for (int w2 = w2_min; w2 <= w2_max; w2++) {
+        // Compute source coordinates for this output pixel
+        const accscalar_t h1r = area_pixel_compute_source_index<accscalar_t>(
+            rheight, h2, align_corners, /*cubic=*/false);
+        const int h1_base = (int)h1r;
+        const int h1p = (h1_base < height1 - 1) ? 1 : 0;
+        const accscalar_t h1lambda = h1r - h1_base;
+        const accscalar_t h0lambda = static_cast<accscalar_t>(1) - h1lambda;
+
+        const accscalar_t w1r = area_pixel_compute_source_index<accscalar_t>(
+            rwidth, w2, align_corners, /*cubic=*/false);
+        const int w1_base = (int)w1r;
+        const int w1p = (w1_base < width1 - 1) ? 1 : 0;
+        const accscalar_t w1lambda = w1r - w1_base;
+        const accscalar_t w0lambda = static_cast<accscalar_t>(1) - w1lambda;
+
+        // Check if our input pixel participates in this interpolation and accumulate all weights
+        // At boundaries, h1p=0 or w1p=0 causes some sampling positions to collapse
+        // to the same pixel, so we need to accumulate weights from all matching positions
+        accscalar_t weight = 0;
+
+        // Check all four interpolation positions and accumulate weights
+        if (h1 == h1_base && w1 == w1_base) {
+          weight += h0lambda * w0lambda;  // top-left
+        }
+        if (h1 == h1_base && w1 == w1_base + w1p) {
+          weight += h0lambda * w1lambda;  // top-right (may be same as top-left if w1p=0)
+        }
+        if (h1 == h1_base + h1p && w1 == w1_base) {
+          weight += h1lambda * w0lambda;  // bottom-left (may be same as top-left if h1p=0)
+        }
+        if (h1 == h1_base + h1p && w1 == w1_base + w1p) {
+          weight += h1lambda * w1lambda;  // bottom-right (may collapse to other positions)
+        }
+
+        if (weight > 0) {
+          const size_t output_idx = nc_idx * height2 * width2 + h2 * width2 + w2;
+          grad_sum += weight * static_cast<accscalar_t>(odata[output_idx]);
+        }
+      }
+    }
+
+    // Write accumulated gradient (no atomics needed)
+    idata[index] = static_cast<scalar_t>(grad_sum);
+  }
+#else
+  const size_t o_numel = nc * width2 * height2;
   for (size_t index = blockDim.x * blockIdx.x + threadIdx.x; index < o_numel;
        index += blockDim.x * gridDim.x) {
     size_t index_temp = index;
@@ -191,6 +280,7 @@ __global__ void upsample_bilinear2d_backward_out_frame(
         static_cast<scalar_t>(h1lambda * w1lambda * d2val),
         true);
   }
+#endif
 }
 
 template <typename scalar_t, typename accscalar_t>
@@ -387,7 +477,6 @@ static void upsample_bilinear2d_backward_out_cuda_template(
   // threads are not covering the whole input tensor.
   grad_input.zero_();
 
-  const size_t num_kernels = nbatch * channels * output_height * output_width;
   const int num_threads = std::min(
       at::cuda::getCurrentDeviceProperties()->maxThreadsPerBlock, 1024);
   cudaStream_t stream = at::cuda::getCurrentCUDAStream();
@@ -397,6 +486,12 @@ static void upsample_bilinear2d_backward_out_cuda_template(
     return;
   }
 
+#ifdef USE_ROCM
+  constexpr bool use_input = true;
+#else
+  constexpr bool use_input = false;
+#endif
+
   AT_DISPATCH_FLOATING_TYPES_AND2(
       at::ScalarType::Half, at::ScalarType::BFloat16,
       grad_output_.scalar_type(), "upsample_bilinear2d_backward_out_frame", [&] {
@@ -414,6 +509,8 @@ static void upsample_bilinear2d_backward_out_cuda_template(
       const accscalar_t rwidth = area_pixel_compute_scale<accscalar_t>(
           input_width, output_width, align_corners, scales_w);
 
+      const size_t num_kernels = nbatch * channels * output_height * output_width;
+
       upsample_bilinear2d_backward_nhwc_out_frame<scalar_t, accscalar_t>
           <<<ceil_div(num_kernels, static_cast<size_t>(num_threads)), num_threads, 0, stream>>>(
               input_height,
@@ -444,6 +541,8 @@ static void upsample_bilinear2d_backward_out_cuda_template(
       const accscalar_t rwidth = area_pixel_compute_scale<accscalar_t>(
           input_width, output_width, align_corners, scales_w);
 
+      const size_t num_kernels = nbatch * channels * (use_input ? input_height * input_width : output_height * output_width);
+
       upsample_bilinear2d_backward_out_frame<scalar_t, accscalar_t>
           <<<ceil_div(num_kernels, static_cast<size_t>(num_threads)),
              num_threads,

aten/src/ATen/native/cuda/fused_adagrad_utils.cuh

@@ -52,7 +52,7 @@ struct FusedAdagradMathFunctor {
   using opmath_t = at::opmath_type<scalar_t>;
 
   C10_DEVICE __forceinline__ void operator()(
-      int64_t chunk_size,
+      int chunk_size,
       FusedOptimizerTensorListMetadata<3>& tl,
       const float* lr_ptr,
       const double& lr,
@@ -133,4 +133,4 @@ struct FusedAdagradMathFunctor {
 
 } // namespace
 
-} // namespace at::native
+} // namespace at::native

aten/src/ATen/native/cuda/reduction_template.cuh

@@ -466,7 +466,7 @@ struct ReduceJitOp {
 
     __syncthreads();
 
-    #if defined(USE_ROCM) || defined(FBCODE_CAFFE2)
+    #ifdef USE_ROCM
     for (int offset = 1; offset < dim_x; offset <<= 1) {
     #else
     for (int offset = dim_x >> 1; offset > 0; offset >>= 1) {

aten/src/ATen/native/cudnn/MHA.cpp

@@ -487,7 +487,9 @@ std::unique_ptr<fe::graph::Graph> build_graph(
   auto scaled_dot_product_flash_attention_options =
       fe::graph::SDPA_attributes()
           .set_name("CUDNN_SDPA")
-          .set_generate_stats(return_softmaxstats)
+          .set_is_inference(return_softmaxstats == false)
+          // TODO(eqy): switch to this API once cuDNN FE is upgraded
+          // .set_generate_stats(return_softmaxstats)
           .set_causal_mask(is_causal)
           .set_attn_scale(attn_scale);
   if (use_ragged_in_dense(q, k, v, o, attn_bias.has_value())) {
@@ -705,7 +707,9 @@ std::unique_ptr<fe::graph::Graph> build_graph_nestedtensor(
   auto scaled_dot_product_flash_attention_options =
       fe::graph::SDPA_attributes()
           .set_name("CUDNN_SDPA_NESTEDTENSOR")
-          .set_generate_stats(return_softmaxstats)
+          .set_is_inference(return_softmaxstats == false)
+          // TODO(eqy): switch to this API once cuDNN FE is upgraded
+          // .set_generate_stats(return_softmaxstats)
           .set_causal_mask(is_causal)
           .set_attn_scale(attn_scale)
           .set_seq_len_q(SEQ_LEN_Q_)

aten/src/ATen/native/mps/kernels/LinearAlgebra.h

@@ -1,16 +0,0 @@
-#pragma onces
-#include <c10/metal/common.h>
-
-template <unsigned N = c10::metal::max_ndim>
-struct OrgqrParams {
-  int32_t num_batch_dims;
-
-  uint32_t m;
-  uint32_t n;
-  uint32_t k;
-
-  ::c10::metal::array<uint32_t, N> A_strides;
-  ::c10::metal::array<uint32_t, N> tau_strides;
-  ::c10::metal::array<uint32_t, N> H_strides;
-  ::c10::metal::array<uint32_t, N> H_sizes;
-};

aten/src/ATen/native/mps/kernels/LinearAlgebra.metal

@@ -1,4 +1,3 @@
-#include <ATen/native/mps/kernels/LinearAlgebra.h>
 #include <c10/metal/utils.h>
 #include <metal_array>
 #include <metal_simdgroup>
@@ -442,7 +441,7 @@ kernel void applySYRK(
     uint3 tid [[thread_position_in_threadgroup]],
     uint3 tgid [[threadgroup_position_in_grid]],
     uint3 tpg [[threads_per_threadgroup]],
-    uint warp_id [[simdgroup_index_in_threadgroup]]) {
+    uint sgitg [[simdgroup_index_in_threadgroup]]) {
   const uint tx = tid.x;
   const uint ty = tid.y;
   const uint simdGroupsPerThreadgroup = (tpg.x * tpg.y + 31) / 32;
@@ -475,8 +474,11 @@ kernel void applySYRK(
       (actSize_j % 8 == 0) && (actSize_h % 8 == 0) && (actSize_k % 8 == 0);
 
   if (use_simdgroup) {
+    uint warp_id = sgitg;
+
     simdgroup_matrix<float, 8, 8> negative_identity =
         simdgroup_matrix<float, 8, 8>(-1.0);
+    simdgroup_matrix<float, 8, 8> identity = simdgroup_matrix<float, 8, 8>(1.0);
     simdgroup_matrix<float, 8, 8> Prod;
     simdgroup_matrix<float, 8, 8> Afrag;
     simdgroup_matrix<float, 8, 8> Bfrag;
@@ -519,7 +521,8 @@ kernel void applySYRK(
             /* transpose = */ upper);
 
         simdgroup_multiply(Prod, Afrag, Bfrag);
-        simdgroup_multiply_accumulate(Cfrag, Prod, negative_identity, Cfrag);
+        simdgroup_multiply(Prod, Prod, negative_identity);
+        simdgroup_multiply_accumulate(Cfrag, Cfrag, identity, Prod);
       }
 
       simdgroup_store(
@@ -641,164 +644,6 @@ kernel void applyPivots(
   }
 }
 
-template <typename T>
-static T bool_to_float(bool b) {
-  return static_cast<T>(b);
-}
-
-template <>
-half2 bool_to_float(bool b) {
-  return half2(b ? 1 : 0, 0);
-}
-
-template <>
-float2 bool_to_float(bool b) {
-  return float2(b ? 1 : 0, 0);
-}
-
-template <typename T>
-static T calc_H_irc(
-    device T* A,
-    uint32_t A_stride_r,
-    uint32_t A_stride_c,
-    constant T* tau,
-    uint32_t tau_stride,
-    uint32_t r,
-    uint32_t c,
-    uint32_t i) {
-  T I_val = bool_to_float<T>(r == c);
-  T tau_val = tau[i * tau_stride];
-
-  T A_ci = c10::metal::conj(A[c * A_stride_r + i * A_stride_c]);
-  T A_ri = A[r * A_stride_r + i * A_stride_c];
-
-  T c_eq_i = bool_to_float<T>(c == i);
-  T r_eq_i = bool_to_float<T>(r == i);
-
-  T A_ci_ = (c > i) ? A_ci : c_eq_i;
-  T A_ri_ = (r > i) ? A_ri : r_eq_i;
-
-  return I_val - c10::metal::mul(tau_val, c10::metal::mul(A_ci_, A_ri_));
-}
-
-// Calculate (A @ B)[r, c], the element in the r-th row and c-th column of the
-// result of matrix multiplying A and B together. A and B must be size m-by-m
-// and have the same strides. The formula for this operation, written in Python
-// syntax, is:
-//   (A @ B)[r, c] = A[r, :].dot(B[:, c])
-template <typename T>
-static T calc_matmul_rc(
-    device T* A,
-    device T* B,
-    uint32_t stride_r,
-    uint32_t stride_c,
-    uint32_t m,
-    uint32_t r,
-    uint32_t c) {
-  T AB_rc = 0;
-  auto A_row_offset = r * stride_r;
-  auto B_col_offset = c * stride_c;
-
-  uint32_t A_col_offset = 0;
-  uint32_t B_row_offset = 0;
-
-  for (uint32_t j = 0; j < m;
-       j++, A_col_offset += stride_c, B_row_offset += stride_r) {
-    AB_rc += c10::metal::mul(
-        A[A_row_offset + A_col_offset], B[B_row_offset + B_col_offset]);
-  }
-  return AB_rc;
-}
-
-template <typename T>
-kernel void orgqr(
-    device T* A [[buffer(0)]],
-    constant T* tau [[buffer(1)]],
-    device T* H [[buffer(2)]],
-    device T* H_prod [[buffer(3)]],
-    constant OrgqrParams<>& params [[buffer(4)]],
-    uint tid [[thread_position_in_grid]]) {
-  constant auto& A_strides = params.A_strides;
-  constant auto& tau_strides = params.tau_strides;
-  constant auto& H_strides = params.H_strides;
-  constant auto& H_sizes = params.H_sizes;
-
-  auto num_batch_dims = params.num_batch_dims;
-  auto m = params.m;
-  auto n = params.n;
-  auto k = params.k;
-
-  auto m2 = m * m;
-  auto batch_idx = tid / m2;
-
-  // Find the matrices for this thread's batch index
-  uint32_t A_offset = 0;
-  uint32_t tau_offset = 0;
-  uint32_t H_offset = 0;
-
-  for (auto dim = num_batch_dims - 1; dim >= 0; dim--) {
-    auto dim_size = H_sizes[dim];
-    auto dim_idx = batch_idx % dim_size;
-
-    A_offset += dim_idx * A_strides[dim];
-    tau_offset += dim_idx * tau_strides[dim];
-    H_offset += dim_idx * H_strides[dim];
-
-    batch_idx /= dim_size;
-  }
-
-  A += A_offset;
-  tau += tau_offset;
-  H += H_offset;
-  H_prod += H_offset;
-
-  auto matrix_idx = tid % m2;
-  auto r = matrix_idx / m;
-  auto c = matrix_idx % m;
-  auto A_stride_r = A_strides[num_batch_dims];
-  auto A_stride_c = A_strides[num_batch_dims + 1];
-  auto tau_stride = tau_strides[num_batch_dims];
-  auto H_stride_r = H_strides[num_batch_dims];
-  auto H_stride_c = H_strides[num_batch_dims + 1];
-
-  // Find the element of H and H_prod that this thread will calculate
-  device T* H_elem_ptr = H + (r * H_stride_r + c * H_stride_c);
-  device T* H_prod_elem_ptr = H_prod + (r * H_stride_r + c * H_stride_c);
-
-  for (uint32_t i = 0; i < k; i++) {
-    // Calculate and write H_i
-
-    T H_irc = calc_H_irc(A, A_stride_r, A_stride_c, tau, tau_stride, r, c, i);
-
-    // Calculate element [r, c] of prod(H_0, ..., H_i)
-    if (i == 0) {
-      *H_prod_elem_ptr = H_irc;
-    } else {
-      *H_elem_ptr = H_irc;
-
-      // Need this sync because the below matmul requires all threads to finish
-      // writing their entries to `H_prod` and `H`.
-      threadgroup_barrier(mem_flags::mem_threadgroup);
-
-      T H_prod_0_to_i_rc =
-          calc_matmul_rc(H_prod, H, H_stride_r, H_stride_c, m, r, c);
-
-      // Need this sync because the above matmul uses the current values in
-      // `H_prod`, and we don't want to overwrite those until all threads are
-      // finished using them.
-      threadgroup_barrier(mem_flags::mem_threadgroup);
-
-      *H_prod_elem_ptr = H_prod_0_to_i_rc;
-    }
-  }
-
-  device T* A_elem_ptr = A + (r * A_stride_r + c * A_stride_c);
-
-  if (c < n) {
-    *A_elem_ptr = *H_prod_elem_ptr;
-  }
-}
-
 #define INSTANTIATE_MM_OPS(DTYPE)                                           \
   template [[host_name("matmul_" #DTYPE)]] kernel void matmul<DTYPE>(       \
       constant DTYPE * mat1Data [[buffer(0)]],                              \
@@ -838,19 +683,3 @@ INSTANTIATE_MM_OPS(int);
 INSTANTIATE_MM_OPS(short);
 INSTANTIATE_MM_OPS(char);
 INSTANTIATE_MM_OPS(uchar);
-
-#define REGISTER_ORGQR(T)                            \
-  template [[host_name("orgqr_" #T)]]                \
-  kernel void orgqr<T>(                              \
-      device T * A [[buffer(0)]],                    \
-      constant T * tau [[buffer(1)]],                \
-      device T * H [[buffer(2)]],                    \
-      device T * H_prod [[buffer(3)]],               \
-      constant OrgqrParams<> & params [[buffer(4)]], \
-      uint tid [[thread_position_in_grid]]);
-
-REGISTER_ORGQR(float);
-REGISTER_ORGQR(half);
-REGISTER_ORGQR(bfloat);
-REGISTER_ORGQR(float2);
-REGISTER_ORGQR(half2);

aten/src/ATen/native/mps/operations/Attention.mm

@@ -92,8 +92,13 @@ static std::tuple<Tensor, Tensor> sdpa_general_mps(const Tensor& query,
           }
 
           // upcasting to float32 if needed to improve precision when multiplying by the scale factor
-          maskedMM = castMPSTensor(mpsGraph, maskedMM, MPSDataTypeFloat32);
+          if ([maskedMM dataType] != MPSDataTypeFloat32) {
+            maskedMM = [mpsGraph castTensor:maskedMM toType:MPSDataTypeFloat32 name:nil];
+          }
           maskedMM = [mpsGraph multiplicationWithPrimaryTensor:maskedMM secondaryTensor:scaleTensor name:nil];
+          if ([maskedMM dataType] != qTensor.dataType) {
+            maskedMM = [mpsGraph castTensor:maskedMM toType:qTensor.dataType name:nil];
+          }
 
           if (is_causal) {
             auto causalMask = [mpsGraph constantWithScalar:1.0f
@@ -107,9 +112,7 @@ static std::tuple<Tensor, Tensor> sdpa_general_mps(const Tensor& query,
                                                       name:nil];
           } else if (attn_mask) {
             graph->maskTensor = mpsGraphRankedPlaceHolder(mpsGraph, *attn_mask);
-            maskedMM = [mpsGraph additionWithPrimaryTensor:maskedMM
-                                           secondaryTensor:castMPSTensor(mpsGraph, graph->maskTensor, maskedMM.dataType)
-                                                      name:nil];
+            maskedMM = [mpsGraph additionWithPrimaryTensor:maskedMM secondaryTensor:graph->maskTensor name:nil];
           }
 
           // Account for case where all values were masked causing division by 0 in softmax (issue:#156707)
@@ -130,8 +133,8 @@ static std::tuple<Tensor, Tensor> sdpa_general_mps(const Tensor& query,
           graph->qTensor = qTensor;
           graph->kTensor = kTensor;
           graph->vTensor = vTensor;
-          graph->outputTensor = castMPSTensor(mpsGraph, output, qTensor.dataType);
-          graph->attnTensor = castMPSTensor(mpsGraph, sm, qTensor.dataType);
+          graph->outputTensor = output;
+          graph->attnTensor = sm;
         });
     auto qPlaceholder = Placeholder(cachedGraph->qTensor, query);
     auto kPlaceholder = Placeholder(cachedGraph->kTensor, key);